CAP Profiles

Bio

Bio

My research lies at the intersection of machine learning, computer vision, healthcare, and computational neuroscience. I work on automatic analysis of human activities and behaviors from videos and connecting how humans perform actions to the brain by analyzing magnetic resonance images (MRIs). I explore explainable machine learning algorithms for understanding the underlying factors of neurodegenerative and neuropsychiatric diseases on the brain as well as their ramifications for everyday life.

Academic Appointments

Administrative Appointments

  • Associate Editor, IEEE Journal of Biomedical and Health Informatics (2020 - Present)
  • Associate Editor, Journal of Ambient Intelligence and Smart Environments (2019 - Present)

Honors & Awards

  • Senior Member, IEEE (2021-Now)
  • REC Fellow, Stanford University Alzheimer's Disease Research Center (ADRC) (2020-2022)
  • Innovator Award 2021, Stanford University School of Medicine Department of Psychiatry \& Behavioral Sciences (2020-2021)
  • Young Investigator Award, Medical Image Computing and Computer Assisted Interventions (MICCAI) (2018)
  • NIH F32 Fellowship Award, NIAAA (2018-2019)

Professional Education

  • Postdoctoral Research Associate, University of North Carolina at Chapel Hill, Machine Learning and Medical Imaging (2017)
  • Graduate Research Scholar, Carnegie Mellon University, Computer Vision (2012)

Contact

Links

Research & Scholarship

Current Research and Scholarly Interests

My research lies in the intersection of machine learning, computer vision, neuroimaging, and computational neuroscience. Particularly, my research focuses on the investigation of different computational and statistical learning-based methods in processing both natural and biomedical images to extract semantics from the underlying visual content. Machine learning, statistics, signal and image processing, neuroscience, computer vision, and neuroimaging have conventionally evolved independently to tackle problems from different perspectives. Occasionally, these concepts neglected each other, while they can offer complementary viewpoints. In recent years, these fields have begun to intertwine, and it is increasingly becoming clear that we need to make use of multidisciplinary research to better process large-scale visual data. I consider my research interests and direction as located at the intersection of all the aforementioned fields.

Teaching

2020-21 Courses

Publications

All Publications

  • Deep End-to-End One-Class Classifier IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Sabokrou, M., Fathy, M., Zhao, G., Adeli, E. 2021; 32 (2): 675–84

    Abstract

    One-class classification (OCC) poses as an essential component in many machine learning and computer vision applications, including novelty, anomaly, and outlier detection systems. With a known definition for a target or normal set of data, one-class classifiers can determine if any given new sample spans within the distribution of the target class. Solving for this task in a general setting is particularly very challenging, due to the high diversity of samples from the target class and the absence of any supervising signal over the novelty (nontarget) concept, which makes designing end-to-end models unattainable. In this article, we propose an adversarial training approach to detect out-of-distribution samples in an end-to-end trainable deep model. To this end, we jointly train two deep neural networks, R and D . The latter plays as the discriminator while the former, during training, helps D characterize a probability distribution for the target class by creating adversarial examples and, during testing, collaborates with it to detect novelties. Using our OCC, we first test outlier detection on two image data sets, Modified National Institute of Standards and Technology (MNIST) and Caltech-256. Then, several experiments for video anomaly detection are performed on University of Minnesota (UMN) and University of California, San Diego (UCSD) data sets. Our proposed method can successfully learn the target class underlying distribution and outperforms other approaches.

    View details for DOI 10.1109/TNNLS.2020.2979049

    View details for Web of Science ID 000616310400017

    View details for PubMedID 32275608

  • Association of Heavy Drinking With Deviant Fiber Tract Development in Frontal Brain Systems in Adolescents. JAMA psychiatry Zhao, Q., Sullivan, E. V., Honnorat, N., Adeli, E., Podhajsky, S., De Bellis, M. D., Voyvodic, J., Nooner, K. B., Baker, F. C., Colrain, I. M., Tapert, S. F., Brown, S. A., Thompson, W. K., Nagel, B. J., Clark, D. B., Pfefferbaum, A., Pohl, K. M. 2020

    Abstract

    Importance: Maturation of white matter fiber systems subserves cognitive, behavioral, emotional, and motor development during adolescence. Hazardous drinking during this active neurodevelopmental period may alter the trajectory of white matter microstructural development, potentially increasing risk for developing alcohol-related dysfunction and alcohol use disorder in adulthood.Objective: To identify disrupted adolescent microstructural brain development linked to drinking onset and to assess whether the disruption is more pronounced in younger rather than older adolescents.Design, Setting, and Participants: This case-control study, conducted from January 13, 2013, to January 15, 2019, consisted of an analysis of 451 participants from the National Consortium on Alcohol and Neurodevelopment in Adolescence cohort. Participants were aged 12 to 21 years at baseline and had at least 2 usable magnetic resonance diffusion tensor imaging (DTI) scans and up to 5 examination visits spanning 4 years. Participants with a youth-adjusted Cahalan score of 0 were labeled as no-to-low drinkers; those with a score of greater than 1 for at least 2 consecutive visits were labeled as heavy drinkers. Exploratory analysis was conducted between no-to-low and heavy drinkers. A between-group analysis was conducted between age- and sex-matched youths, and a within-participant analysis was performed before and after drinking.Exposures: Self-reported alcohol consumption in the past year summarized by categorical drinking levels.Main Outcomes and Measures: Diffusion tensor imaging measurement of fractional anisotropy (FA) in the whole brain and fiber systems quantifying the developmental change of each participant as a slope.Results: Analysis of whole-brain FA of 451 adolescents included 291 (64.5%) no-to-low drinkers and 160 (35.5%) heavy drinkers who indicated the potential for a deleterious association of alcohol with microstructural development. Among the no-to-low drinkers, 142 (48.4%) were boys with mean (SD) age of 16.5 (2.2) years and 149 (51.2%) were girls with mean (SD) age of 16.5 (2.1) years and 192 (66.0%) were White participants. Among the heavy drinkers, 86 (53.8%) were boys with mean (SD) age of 20.1 (1.5) years and 74 (46.3%) were girls with mean (SD) age of 20.5 (2.0) years and 142 (88.8%) were White participants. A group analysis revealed FA reduction in heavy-drinking youth compared with age- and sex-matched controls (t154=-2.7, P=.008). The slope of this reduction correlated with log of days of drinking since the baseline visit (r156=-0.21, 2-tailed P=.008). A within-participant analysis contrasting developmental trajectories of youths before and after they initiated heavy drinking supported the prediction that drinking onset was associated with and potentially preceded disrupted white matter integrity. Age-alcohol interactions (t152=3.0, P=.004) observed for the FA slopes indicated that the alcohol-associated disruption was greater in younger than older adolescents and was most pronounced in the genu and body of the corpus callosum, regions known to continue developing throughout adolescence.Conclusions and Relevance: This case-control study of adolescents found a deleterious association of alcohol use with white matter microstructural integrity. These findings support the concept of heightened vulnerability to environmental agents, including alcohol, associated with attenuated development of major white matter tracts in early adolescence.

    View details for DOI 10.1001/jamapsychiatry.2020.4064

    View details for PubMedID 33377940

  • Ethical issues in using ambient intelligence in health-care settings. The Lancet. Digital health Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S. S., Wieten, S., Cho, M. K., Magnus, D., Fei-Fei, L., Schulman, K., Milstein, A. 2020

    Abstract

    Ambient intelligence is increasingly finding applications in health-care settings, such as helping to ensure clinician and patient safety by monitoring staff compliance with clinical best practices or relieving staff of burdensome documentation tasks. Ambient intelligence involves using contactless sensors and contact-based wearable devices embedded in health-care settings to collect data (eg, imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data. Despite the promise of ambient intelligence to improve quality of care, the continuous collection of large amounts of sensor data in health-care settings presents ethical challenges, particularly in terms of privacy, data management, bias and fairness, and informed consent. Navigating these ethical issues is crucial not only for the success of individual uses, but for acceptance of the field as a whole.

    View details for DOI 10.1016/S2589-7500(20)30275-2

    View details for PubMedID 33358138

  • Guest Editorial: AI-Powered 3D Vision IET IMAGE PROCESSING Yang, Y., Yang, J., Adeli, E. 2020; 14 (12): 2627–29

    View details for DOI 10.1049/iet-ipr.2020.1194

    View details for Web of Science ID 000582146100001

  • Depth map artefacts reduction: a review IET IMAGE PROCESSING Ibrahim, M., Liu, Q., Khan, R., Yang, J., Adeli, E., Yang, Y. 2020; 14 (12): 2630–44

    View details for DOI 10.1049/iet-ipr.2019.1622

    View details for Web of Science ID 000582146100002

  • Socially and Contextually Aware Human Motion and Pose Forecasting IEEE ROBOTICS AND AUTOMATION LETTERS Adeli, V., Adeli, E., Reid, I., Niebles, J., Rezatofighi, H. 2020; 5 (4): 6033–40

    View details for DOI 10.1109/LRA.2020.3010742

    View details for Web of Science ID 000554894900027

  • Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Gadgil, S., Zhao, Q., Pfefferbaum, A., Sullivan, E. V., Adeli, E., Pohl, K. M. 2020; 12267: 528–38

    Abstract

    The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain. However, existing deep learning methods applied to rs-fMRI either neglect the functional dependency between different brain regions in a network or discard the information in the temporal dynamics of brain activity. To overcome those shortcomings, we propose to formulate functional connectivity networks within the context of spatio-temporal graphs. We train a spatio-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. Simultaneously, the model learns the importance of graph edges within ST-GCN to gain insight into the functional connectivities contributing to the prediction. In analyzing the rs-fMRI of the Human Connectome Project (HCP, N = 1,091) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N = 773), ST-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals. Furthermore, the brain regions and functional connections significantly contributing to the predictions of our model are important markers according to the neuroscience literature.

    View details for DOI 10.1007/978-3-030-59728-3_52

    View details for PubMedID 33257918

  • Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Lu, M., Poston, K., Pfefferbaum, A., Sullivan, E. V., Fei-Fei, L., Pohl, K. M., Niebles, J. C., Adeli, E. 2020; 12263: 637–47

    Abstract

    Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F 1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments. The code is available at https://github.com/mlu355/PD-Motor-Severity-Estimation.

    View details for DOI 10.1007/978-3-030-59716-0_61

    View details for PubMedID 33103164

  • Deep Learning Identifies Morphological Determinants of Sex Differences in the Pre-Adolescent Brain. NeuroImage Adeli, E., Zhao, Q., Zahr, N. M., Goldstone, A., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2020: 117293

    Abstract

    The application of data-driven deep learning to identify sex differences in developing brain structures of pre-adolescents has heretofore not been accomplished. Here, the approach identifies sex differences by analyzing the minimally processed MRIs of the first 8,144 participants (age 9 and 10 years) recruited by the Adolescent Brain Cognitive Development (ABCD) study. The identified pattern accounted for confounding factors (i.e., head size, age, puberty development, socioeconomic status) and comprised cerebellar (corpus medullare, lobules III, IV/V, and VI) and subcortical (pallidum, amygdala, hippocampus, parahippocampus, insula, putamen) structures. While these have been individually linked to expressing sex differences, a novel discovery was that their grouping accurately predicted the sex in individual pre-adolescents. Another novelty was relating differences specific to the cerebellum to pubertal development. Finally, we found that reducing the pattern to a single score not only accurately predicted sex but also correlated with cognitive behavior linked to working memory. The predictive power of this score and the constellation of identified brain structures provide evidence for sex differences in pre-adolescent neurodevelopment and may augment understanding of sex-specific vulnerability or resilience to psychiatric disorders and presage sex-linked learning disabilities.

    View details for DOI 10.1016/j.neuroimage.2020.117293

    View details for PubMedID 32841716

  • Segmenting the Future IEEE ROBOTICS AND AUTOMATION LETTERS Chiu, H., Adeli, E., Niebles, J. 2020; 5 (3): 4202–9

    View details for DOI 10.1109/LRA.2020.2992184

    View details for Web of Science ID 000541731600001

  • Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography IEEE TRANSACTIONS ON MEDICAL IMAGING Eslami, M., Tabarestani, S., Albarqouni, S., Adeli, E., Navab, N., Adjouadi, M. 2020; 39 (7): 2553–65

    Abstract

    Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segmentation but performed separately, limiting any learning that comes with the consolidation of parameters that could optimize both processes. This study, and for the first time, introduces a multitask deep learning model that generates simultaneously the bone-suppressed image and the organ-segmented image, enhancing the accuracy of tasks, minimizing the number of parameters needed by the model and optimizing the processing time, all by exploiting the interplay between the network parameters to benefit the performance of both tasks. The architectural design of this model, which relies on a conditional generative adversarial network, reveals the process on how the wellestablished pix2pix network (image-to-image network) is modified to fit the need for multitasking and extending it to the new image-to-images architecture. The developed source code of this multitask model is shared publicly on Github as the first attempt for providing the two-task pix2pix extension, a supervised/paired/aligned/registered image-to-images translation which would be useful in many multitask applications. Dilated convolutions are also used to improve the results through a more effective receptive field assessment. The comparison with state-of-the-art al-gorithms along with ablation study and a demonstration video1 are provided to evaluate the efficacy and gauge the merits of the proposed approach.

    View details for DOI 10.1109/TMI.2020.2974159

    View details for Web of Science ID 000545410200024

    View details for PubMedID 32078541

  • Skeleton-based structured early activity prediction MULTIMEDIA TOOLS AND APPLICATIONS Arzani, M. M., Fathy, M., Azirani, A. A., Adeli, E. 2020

    View details for DOI 10.1007/s11042-020-08875-w

    View details for Web of Science ID 000528439200002

  • Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction IEEE ROBOTICS AND AUTOMATION LETTERS Liu, B., Adeli, E., Cao, Z., Lee, K., Shenoi, A., Gaidon, A., Niebles, J. 2020; 5 (2): 3485–92

    View details for DOI 10.1109/LRA.2020.2976305

    View details for Web of Science ID 000520954200034

  • Mammographic mass segmentation using multichannel and multiscale fully convolutional networks INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY Xu, S., Adeli, E., Cheng, J., Xiang, L., Li, Y., Lee, S., Shen, D. 2020

    View details for DOI 10.1002/ima.22423

    View details for Web of Science ID 000521597700001

  • Population-guided large margin classifier for high-dimension low-sample-size problems PATTERN RECOGNITION Yin, Q., Adeli, E., Shen, L., Shen, D. 2020; 97

    View details for DOI 10.1016/j.patcog.2019.107030

    View details for Web of Science ID 000491609400009

  • Training confounder-free deep learning models for medical applications. Nature communications Zhao, Q. n., Adeli, E. n., Pohl, K. M. 2020; 11 (1): 6010

    Abstract

    The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variables (e.g., diagnosis). Improper modeling of those relationships often results in spurious and biased associations. Traditional machine learning and statistical models minimize the impact of confounders by, for example, matching data sets, stratifying data, or residualizing imaging measurements. Alternative strategies are needed for state-of-the-art deep learning models that use end-to-end training to automatically extract informative features from large set of images. In this article, we introduce an end-to-end approach for deriving features invariant to confounding factors while accounting for intrinsic correlations between the confounder(s) and prediction outcome. The method does so by exploiting concepts from traditional statistical methods and recent fair machine learning schemes. We evaluate the method on predicting the diagnosis of HIV solely from Magnetic Resonance Images (MRIs), identifying morphological sex differences in adolescence from those of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), and determining the bone age from X-ray images of children. The results show that our method can accurately predict while reducing biases associated with confounders. The code is available at https://github.com/qingyuzhao/br-net .

    View details for DOI 10.1038/s41467-020-19784-9

    View details for PubMedID 33243992

  • Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision Mangalam, K., Adeli, E., Lee, K., Gaidon, A., Niebles, J., IEEE Comp Soc IEEE COMPUTER SOC. 2020: 2773–82

    View details for Web of Science ID 000578444802088

  • Adolescent alcohol use disrupts functional neurodevelopment in sensation seeking girls. Addiction biology Zhao, Q. n., Sullivan, E. V., Műller-Oehring, E. M., Honnorat, N. n., Adeli, E. n., Podhajsky, S. n., Baker, F. C., Colrain, I. M., Prouty, D. n., Tapert, S. F., Brown, S. A., Meloy, M. J., Brumback, T. n., Nagel, B. J., Morales, A. M., Clark, D. B., Luna, B. n., De Bellis, M. D., Voyvodic, J. T., Nooner, K. B., Pfefferbaum, A. n., Pohl, K. M. 2020: e12914

    Abstract

    Exogenous causes, such as alcohol use, and endogenous factors, such as temperament and sex, can modulate developmental trajectories of adolescent neurofunctional maturation. We examined how these factors affect sexual dimorphism in brain functional networks in youth drinking below diagnostic threshold for alcohol use disorder (AUD). Based on the 3-year, annually acquired, longitudinal resting-state functional magnetic resonance imaging (MRI) data of 526 adolescents (12-21 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) cohort, developmental trajectories of 23 intrinsic functional networks (IFNs) were analyzed for (1) sexual dimorphism in 259 participants who were no-to-low drinkers throughout this period; (2) sex-alcohol interactions in two age- and sex-matched NCANDA subgroups (N = 76 each), half no-to-low, and half moderate-to-heavy drinkers; and (3) moderating effects of gender-specific alcohol dose effects and a multifactorial impulsivity measure on IFN connectivity in all NCANDA participants. Results showed that sex differences in no-to-low drinkers diminished with age in the inferior-occipital network, yet girls had weaker within-network connectivity than boys in six other networks. Effects of adolescent alcohol use were more pronounced in girls than boys in three IFNs. In particular, girls showed greater within-network connectivity in two motor networks with more alcohol consumption, and these effects were mediated by sensation-seeking only in girls. Our results implied that drinking might attenuate the naturally diminishing sexual differences by disrupting the maturation of network efficiency more severely in girls. The sex-alcohol-dose effect might explain why women are at higher risk of alcohol-related health and psychosocial consequences than men.

    View details for DOI 10.1111/adb.12914

    View details for PubMedID 32428984

  • FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation. Neuroinformatics Zhu, H. n., Adeli, E. n., Shi, F. n., Shen, D. n. 2020

    Abstract

    Segmentation of medical images using multiple atlases has recently gained immense attention due to their augmented robustness against variabilities across different subjects. These atlas-based methods typically comprise of three steps: atlas selection, image registration, and finally label fusion. Image registration is one of the core steps in this process, accuracy of which directly affects the final labeling performance. However, due to inter-subject anatomical variations, registration errors are inevitable. The aim of this paper is to develop a deep learning-based confidence estimation method to alleviate the potential effects of registration errors. We first propose a fully convolutional network (FCN) with residual connections to learn the relationship between the image patch pair (i.e., patches from the target subject and the atlas) and the related label confidence patch. With the obtained label confidence patch, we can identify the potential errors in the warped atlas labels and correct them. Then, we use two label fusion methods to fuse the corrected atlas labels. The proposed methods are validated on a publicly available dataset for hippocampus segmentation. Experimental results demonstrate that our proposed methods outperform the state-of-the-art segmentation methods.

    View details for DOI 10.1007/s12021-019-09448-5

    View details for PubMedID 31898145

  • Confounder-Aware Visualization of ConvNets. Machine learning in medical imaging. MLMI (Workshop) Zhao, Q., Adeli, E., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2019; 11861: 328–36

    Abstract

    With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images. To gain a better understanding of how a disease impacts the brain, the studies visualize the salience maps of the ConvNet highlighting voxels within the brain majorly contributing to the prediction. However, these salience maps are generally confounded, i.e., some salient regions are more predictive of confounding variables (such as age) than the diagnosis. To avoid such misinterpretation, we propose in this paper an approach that aims to visualize confounder-free saliency maps that only highlight voxels predictive of the diagnosis. The approach incorporates univariate statistical tests to identify confounding effects within the intermediate features learned by ConvNet. The influence from the subset of confounded features is then removed by a novel partial back-propagation procedure. We use this two-step approach to visualize confounder-free saliency maps extracted from synthetic and two real datasets. These experiments reveal the potential of our visualization in producing unbiased model-interpretation.

    View details for DOI 10.1007/978-3-030-32692-0_38

    View details for PubMedID 32549051

  • Data Augmentation Based on Substituting Regional MRIs Volume Scores. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention : International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, held in c... Leng, T., Zhao, Q., Yang, C., Lu, Z., Adeli, E., Pohl, K. M. 2019; 11851: 32–41

    Abstract

    Due to difficulties in collecting sufficient training data, recent advances in neural-network-based methods have not been fully explored in the analysis of brain Magnetic Resonance Imaging (MRI). A possible solution to the limited-data issue is to augment the training set with synthetically generated data. In this paper, we propose a data augmentation strategy based on regional feature substitution. We demonstrate the advantages of this strategy with respect to training a simple neural-network-based classifier in predicting when individual youth transition from no-to-low to medium-to-heavy alcohol drinkers solely based on their volumetric MRI measurements. Based on 20-fold cross-validation, we generate more than one million synthetic samples from less than 500 subjects for each training run. The classifier achieves an accuracy of 74.1% in correctly distinguishing non-drinkers from drinkers at baseline and a 43.2% weighted accuracy in predicting the transition over a three year period (5-group classification task). Both accuracy scores are significantly better than training the classifier on the original dataset.

    View details for DOI 10.1007/978-3-030-33642-4_4

    View details for PubMedID 32924031

  • Covariance Shrinkage for Dynamic Functional Connectivity. Connectomics in neuroImaging : third International Workshop, CNI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings. CNI (Workshop) (3rd : 2019 : Shenzhen Shi, China) Honnorat, N., Adeli, E., Zhao, Q., Pfefferbaum, A., Sullivan, E. V., Pohl, K. 2019; 11848: 32–41

    Abstract

    The tracking of dynamic functional connectivity (dFC) states in resting-state fMRI scans aims to reveal how the brain sequentially processes stimuli and thoughts. Despite the recent advances in statistical methods, estimating the high dimensional dFC states from a small number of available time points remains a challenge. This paper shows that the challenge is reduced by linear covariance shrinkage, a statistical method used for the estimation of large covariance matrices from small number of samples. We present a computationally efficient formulation of our approach that scales dFC analysis up to full resolution resting-state fMRI scans. Experiments on synthetic data demonstrate that our approach produces dFC estimates that are closer to the ground-truth than state-of-the-art estimation approaches. When comparing methods on the rs-fMRI scans of 162 subjects, we found that our approach is better at extracting functional networks and capturing differences in rs-fMRI acquisition and diagnosis.

    View details for DOI 10.1007/978-3-030-32391-2_4

    View details for PubMedID 32924030

  • High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Zhou, S., Nie, D., Adeli, E., Yin, J., Lian, J., Shen, D. 2019

    Abstract

    Automatic image segmentation is an essential step for many medical image analysis applications, include computer-aided radiation therapy, disease diagnosis, and treatment effect evaluation. One of the major challenges for this task is the blurry nature of medical images (e.g., CT, MR and, microscopic images), which can often result in low-contrast and vanishing boundaries. With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder-decoder deep architectures. However, in many applications (with adjacent targets in blurry images), these models often fail to accurately locate complex boundaries and properly segment tiny isolated parts. In this paper, we aim to provide a method for blurry medical image segmentation and argue that skip connections are not enough to help accurately locate indistinct boundaries. Accordingly, we propose a novel high-resolution multi-scale encoder-decoder network (HMEDN), in which multi-scale dense connections are introduced for the encoder-decoder structure to finely exploit comprehensive semantic information. Besides skip connections, extra deeply-supervised high-resolution pathways (comprised of densely connected dilated convolutions) are integrated to collect high-resolution semantic information for accurate boundary localization. These pathways are paired with a difficulty-guided cross-entropy loss function and a contour regression task to enhance the quality of boundary detection. Extensive experiments on a pelvic CT image dataset, a multi-modal brain tumor dataset, and a cell segmentation dataset show the effectiveness of our method for 2D/3D semantic segmentation and 2D instance segmentation, respectively. Our experimental results also show that besides increasing the network complexity, raising the resolution of semantic feature maps can largely affect the overall model performance. For different tasks, finding a balance between these two factors can further improve the performance of the corresponding network.

    View details for DOI 10.1109/TIP.2019.2919937

    View details for PubMedID 31226074

  • Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Liu, M., Zhang, J., Adeli, E., Shen, D. 2019; 66 (5): 1195–1206

    Abstract

    In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain diseases and predicting clinical scores using magnetic resonance imaging (MRI) have attracted increasing attention since these two tasks are highly correlated. Most of existing joint learning approaches require hand-crafted feature representations for MR images. Since hand-crafted features of MRI and classification/regression models may not coordinate well with each other, conventional methods may lead to sub-optimal learning performance. Also, demographic information (e.g., age, gender, and education) of subjects may also be related to brain status, and thus can help improve the diagnostic performance. However, conventional joint learning methods seldom incorporate such demographic information into the learning models. To this end, we propose a deep multi-task multi-channel learning (DM 2L) framework for simultaneous brain disease classification and clinical score regression, using MRI data and demographic information of subjects. Specifically, we first identify the discriminative anatomical landmarks from MR images in a data-driven manner, and then extract multiple image patches around these detected landmarks. We then propose a deep multi-task multi-channel convolutional neural network for joint classification and regression. Our DM 2L framework can not only automatically learn discriminative features for MR images, but also explicitly incorporate the demographic information of subjects into the learning process. We evaluate the proposed method on four large multi-center cohorts with 1984 subjects, and the experimental results demonstrate that DM 2L is superior to several state-of-the-art joint learning methods in both the tasks of disease classification and clinical score regression.

    View details for DOI 10.1109/TBME.2018.2869989

    View details for Web of Science ID 000466024600001

    View details for PubMedID 30222548

    View details for PubMedCentralID PMC6764421

  • Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning IEEE TRANSACTIONS ON MEDICAL IMAGING Zhang, C., Adeli, E., Wu, Z., Li, G., Lin, W., Shen, D. 2019; 38 (4): 909–18

    Abstract

    The early postnatal period witnesses rapid and dynamic brain development. However, the relationship between brain anatomical structure and cognitive ability is still unknown. Currently, there is no explicit model to characterize this relationship in the literature. In this paper, we explore this relationship by investigating the mapping between morphological features of the cerebral cortex and cognitive scores. To this end, we introduce a multi-view multi-task learning approach to intuitively explore complementary information from different time-points and handle the missing data issue in longitudinal studies simultaneously. Accordingly, we establish a novel model, latent partial multi-view representation learning. Our approach regards data from different time-points as different views and constructs a latent representation to capture the complementary information from incomplete time-points. The latent representation explores the complementarity across different time-points and improves the accuracy of prediction. The minimization problem is solved by the alternating direction method of multipliers. Experimental results on both synthetic and real data validate the effectiveness of our proposed algorithm.

    View details for DOI 10.1109/TMI.2018.2874964

    View details for Web of Science ID 000463608000004

    View details for PubMedID 30307859

    View details for PubMedCentralID PMC6450718

  • Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals. Biological psychiatry. Cognitive neuroscience and neuroimaging Adeli, E., Zahr, N. M., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2019

    Abstract

    The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study documents complex radiologically identified, neuroanatomical effects of AUD+HIV comorbidity by identifying structural brain systems that predicted diagnosis on an individual basis. Applying novel machine learning analysis to 549 participants (199 control subjects, 222 with AUD, 68 with HIV, 60 with AUD+HIV), 298 magnetic resonance imaging brain measurements were automatically reduced to small subsets per group. Significance of each diagnostic pattern was inferred from its accuracy in predicting diagnosis and performance on six cognitive measures. While all three diagnostic patterns predicted the learning and memory score, the AUD+HIV pattern was the largest and had the highest predication accuracy (78.1%). Providing a roadmap for analyzing large, multimodal datasets, the machine learning analysis revealed imaging phenotypes that predicted diagnostic membership of magnetic resonance imaging scans of individuals with AUD, HIV, and their comorbidity.

    View details for DOI 10.1016/j.bpsc.2019.02.003

    View details for PubMedID 30982583

  • 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation IEEE TRANSACTIONS ON CYBERNETICS Nie, D., Wang, L., Adeli, E., Lao, C., Lin, W., Shen, D. 2019; 49 (3): 1123–36

    Abstract

    Accurate segmentation of infant brain images into different regions of interest is one of the most important fundamental steps in studying early brain development. In the isointense phase (approximately 6-8 months of age), white matter and gray matter exhibit similar levels of intensities in magnetic resonance (MR) images, due to the ongoing myelination and maturation. This results in extremely low tissue contrast and thus makes tissue segmentation very challenging. Existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single modality. To address the challenge, we propose a novel 3-D multimodal fully convolutional network (FCN) architecture for segmentation of isointense phase brain MR images. Specifically, we extend the conventional FCN architectures from 2-D to 3-D, and, rather than directly using FCN, we intuitively integrate coarse (naturally high-resolution) and dense (highly semantic) feature maps to better model tiny tissue regions, in addition, we further propose a transformation module to better connect the aggregating layers; we also propose a fusion module to better serve the fusion of feature maps. We compare the performance of our approach with several baseline and state-of-the-art methods on two sets of isointense phase brain images. The comparison results show that our proposed 3-D multimodal FCN model outperforms all previous methods by a large margin in terms of segmentation accuracy. In addition, the proposed framework also achieves faster segmentation results compared to all other methods. Our experiments further demonstrate that: 1) carefully integrating coarse and dense feature maps can considerably improve the segmentation performance; 2) batch normalization can speed up the convergence of the networks, especially when hierarchical feature aggregations occur; and 3) integrating multimodal information can further boost the segmentation performance.

    View details for DOI 10.1109/TCYB.2018.2797905

    View details for Web of Science ID 000458655900033

    View details for PubMedID 29994385

    View details for PubMedCentralID PMC6230311

  • Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Adeli, E., Thung, K., An, L., Wu, G., Shi, F., Wang, T., Shen, D. 2019; 41 (2): 515–22

    Abstract

    Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values (feature-noises). Methods robust to both types of these deviations are somewhat overlooked in the literature. We further argue that denoising can be more effective, if we learn the model using all the available labeled and unlabeled samples, as the intrinsic geometry of the sample manifold can be better constructed using more data points. In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis to detect sample-outliers and feature-noises simultaneously, using both labeled training and unlabeled testing data. We conduct several experiments on a synthetic, some benchmark semi-supervised learning, and two brain neurodegenerative disease diagnosis datasets (for Parkinson's and Alzheimer's diseases). Specifically for the application of neurodegenerative diseases diagnosis, incorporating robust machine learning methods can be of great benefit, due to the noisy nature of neuroimaging data. Our results show that our method outperforms the baseline and several state-of-the-art methods, in terms of both accuracy and the area under the ROC curve.

    View details for DOI 10.1109/TPAMI.2018.2794470

    View details for Web of Science ID 000456150600018

    View details for PubMedID 29994560

    View details for PubMedCentralID PMC6050136

  • Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages SCIENTIFIC REPORTS Nie, D., Lu, J., Zhang, H., Adeli, E., Wang, J., Yu, Z., Liu, L., Wang, Q., Wu, J., Shen, D. 2019; 9: 1103

    Abstract

    High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inaccurate. Recent radiomics studies have shown better prognosis by using carefully-engineered image features from magnetic resonance images (MRI). However, feature engineering is usually time consuming, laborious and subjective. Most importantly, the engineered features cannot effectively encode other predictive but implicit information provided by multi-modal neuroimages. We propose a two-stage learning-based method to predict the overall survival (OS) time of high-grade gliomas patient. At the first stage, we adopt deep learning, a recently dominant technique of artificial intelligence, to automatically extract implicit and high-level features from multi-modal, multi-channel preoperative MRI such that the features are competent of predicting survival time. Specifically, we utilize not only contrast-enhanced T1 MRI, but also diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI), for computing multiple metric maps (including various diffusivity metric maps derived from DTI, and also the frequency-specific brain fluctuation amplitude maps and local functional connectivity anisotropy-related metric maps derived from rs-fMRI) from 68 high-grade glioma patients with different survival time. We propose a multi-channel architecture of 3D convolutional neural networks (CNNs) for deep learning upon those metric maps, from which high-level predictive features are extracted for each individual patch of these maps. At the second stage, those deeply learned features along with the pivotal limited demographic and tumor-related features (such as age, tumor size and histological type) are fed into a support vector machine (SVM) to generate the final prediction result (i.e., long or short overall survival time). The experimental results demonstrate that this multi-model, multi-channel deep survival prediction framework achieves an accuracy of 90.66%, outperforming all the competing methods. This study indicates highly demanded effectiveness on prognosis of deep learning technique in neuro-oncological applications for better individualized treatment planning towards precision medicine.

    View details for DOI 10.1038/s41598-018-37387-9

    View details for Web of Science ID 000457287000091

    View details for PubMedID 30705340

    View details for PubMedCentralID PMC6355868

  • Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data NEUROIMAGE Adeli, E., Meng, Y., Li, G., Lin, W., Shen, D. 2019; 185: 783–92

    Abstract

    Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brain development. To precisely chart the early brain developmental trajectories, longitudinal studies with data acquired over a long-enough period of infants' early life is essential. However, in practice, missing data from different time point(s) during the data gathering procedure is often inevitable. This leads to incomplete set of longitudinal data, which poses a major challenge for such studies. In this paper, prediction of multiple future cognitive scores with incomplete longitudinal imaging data is modeled into a multi-task machine learning framework. To efficiently learn this model, we account for selection of informative features (i.e., neuroimaging morphometric measurements for different time points), while preserving the structural information and the interrelation between these multiple cognitive scores. Several experiments are conducted on a carefully acquired in-house dataset, and the results affirm that we can predict the cognitive scores measured at the age of four years old, using the imaging data of earlier time points, as early as 24 months of age, with a reasonable performance (i.e., root mean square error of 0.18).

    View details for DOI 10.1016/j.neuroimage.2018.04.052

    View details for Web of Science ID 000451628200066

    View details for PubMedID 29709627

    View details for PubMedCentralID PMC6204112

  • Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection. IEEE transactions on pattern analysis and machine intelligence Adeli, E. n., Li, X. n., Kwon, D. n., Zhang, Y. n., Pohl, K. n. 2019

    Abstract

    Many vision-based applications rely on logistic regression for embedding classification within a probabilistic context, such as recognition in images and videos or identifying disease-specific image phenotypes from neuroimages. Logistic regression, however, often performs poorly when trained on data that is noisy, has irrelevant features, or when the samples are distributed across the classes in an imbalanced setting; a common occurrence in visual recognition tasks. To deal with those issues, researchers generally rely on ad-hoc regularization techniques or model a subset of these issues. We instead propose a mathematically sound logistic regression model that selects a subset of (relevant) features and (informative and balanced) set of samples during the training process. The model does so by applying cardinality constraints (via l0 -'norm' sparsity) on the features and samples. l0 defines sparsity in mathematical settings but in practice has mostly been approximated (e.g., via l1 or its variations) for computational simplicity. We prove that a local minimum to the non-convex optimization problems induced by cardinality constraints can be computed by combining block coordinate descent with penalty decomposition. On synthetic, image recognition, and neuroimaging datasets, we furthermore show that the accuracy of the method is higher than alternative methods and classifiers commonly used in the literature.

    View details for DOI 10.1109/TPAMI.2019.2901688

    View details for PubMedID 30835210

  • Imitation Learning for Human Pose Prediction Wang, B., Adeli, E., Chiu, H., Huang, D., Niebles, J., IEEE IEEE. 2019: 7123–32

    View details for DOI 10.1109/ICCV.2019.00722

    View details for Web of Science ID 000548549202023

  • Variational AutoEncoder for Regression: Application to Brain Aging Analysis Zhao, Q., Adeli, E., Honnorat, N., Leng, T., Pohl, K. M., Shen, D., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P. T., Khan, A. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 823–31

    Abstract

    While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified probabilistic model for learning the latent space of imaging data and performing supervised regression. Based on recent advances in learning disentangled representations, the novel generative process explicitly models the conditional distribution of latent representations with respect to the regression target variable. Performing a variational inference procedure on this model leads to joint regularization between the VAE and a neural-network regressor. In predicting the age of 245 subjects from their structural Magnetic Resonance (MR) images, our model is more accurate than state-of-the-art methods when applied to either region-of-interest (ROI) measurements or raw 3D volume images. More importantly, unlike simple feed-forward neural-networks, disentanglement of age in latent representations allows for intuitive interpretation of the structural developmental patterns of the human brain.

    View details for DOI 10.1007/978-3-030-32245-8_91

    View details for Web of Science ID 000548438900091

    View details for PubMedID 32705091

    View details for PubMedCentralID PMC7377006

  • Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis. Information processing in medical imaging : proceedings of the ... conference Zhao, Q. n., Honnorat, N. n., Adeli, E. n., Pohl, K. M. 2019; 11492: 867–79

    Abstract

    Resting-state functional connectivity states are often identified as clusters of dynamic connectivity patterns. However, existing clustering approaches do not distinguish major states from rarely occurring minor states and hence are sensitive to noise. To address this issue, we propose to model major states using a non-linear generative process guided by a Gaussian-mixture distribution in a low-dimensional latent space, while separately modeling the connectivity patterns of minor states by a non-informative uniform distribution. We embed this truncated Gaussian-Mixture model in a Variational Autoencoder framework to obtain a general joint clustering and outlier detection approach, tGM-VAE. When applied to synthetic data with known ground-truth, tGM-VAE is more accurate in clustering connectivity patterns than existing approaches. On the rs-fMRI of 593 healthy adolescents, tGM-VAE identifies meaningful major connectivity states. The dwell time of these states significantly correlates with age.

    View details for DOI 10.1007/978-3-030-20351-1_68

    View details for PubMedID 32699491

    View details for PubMedCentralID PMC7375028

  • AVID: Adversarial Visual Irregularity Detection Sabokrou, M., Pourreza, M., Fayyaz, M., Entezari, R., Fathy, M., Gall, J., Adeli, E., Jawahar, C. V., Li, H., Mori, G., Schindler, K. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 488–505

    View details for DOI 10.1007/978-3-030-20876-9_31

    View details for Web of Science ID 000492905500031

  • UNSUPERVISED FEATURE RANKING AND SELECTION BASED ON AUTOENCODERS Sharifipour, S., Fayyazi, H., Sabokrou, M., Adeli, E., IEEE IEEE. 2019: 3172–76

    View details for Web of Science ID 000482554003079

  • Difficulty-Aware Attention Network with Confidence Learning for Medical Image Segmentation Nie, D., Wang, L., Xiang, L., Zhou, S., Adeli, E., Shen, D., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2019: 1085–92

    View details for Web of Science ID 000485292601012

  • Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis Zhao, Q., Honnorat, N., Adeli, E., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Chung, A. C., Gee, J. C., Yushkevich, P. A., Bao, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 867–79

    View details for DOI 10.1007/978-3-030-20351-1_68

    View details for Web of Science ID 000493380900068

  • Action-Agnostic Human Pose Forecasting Chiu, H., Adeli, E., Wang, B., Huang, D., Niebles, J., IEEE IEEE. 2019: 1423–32

    View details for DOI 10.1109/WACV.2019.00156

    View details for Web of Science ID 000469423400149

  • Chained regularization for identifying brain patterns specific to HIV infection NEUROIMAGE Adeli, E., Kwon, D., Zhao, Q., Pfefferbaum, A., Zahr, N. M., Sullivan, E. V., Pohl, K. M. 2018; 183: 425–37

    View details for DOI 10.1016/j.neuroimage.2018.08.022

    View details for Web of Science ID 000447750200038

  • Chained regularization for identifying brain patterns specific to HIV infection. NeuroImage Adeli, E., Kwon, D., Zhao, Q., Pfefferbaum, A., Zahr, N. M., Sullivan, E. V., Pohl, K. M. 2018

    Abstract

    Human Immunodeficiency Virus (HIV) infection continues to have major adverse public health and clinical consequences despite the effectiveness of combination Antiretroviral Therapy (cART) in reducing HIV viral load and improving immune function. As successfully treated individuals with HIV infection age, their cognition declines faster than reported for normal aging. This phenomenon underlines the importance of improving long-term care, which requires better understanding of the impact of HIV on the brain. In this paper, automated identification of patients and brain regions affected by HIV infection are modeled as a classification problem, whose solution is determined in two steps within our proposed Chained-Regularization framework. The first step focuses on selecting the HIV pattern (i.e., the most informative constellation of brain region measurements for distinguishing HIV infected subjects from healthy controls) by constraining the search for the optimal parameter setting of the classifier via group sparsity (ℓ2,1-norm). The second step improves classification accuracy by constraining the parameterization with respect to the selected measurements and the Euclidean regularization (ℓ2-norm). When applied to the cortical and subcortical structural Magnetic Resonance Images (MRI) measurements of 65 controls and 65 HIV infected individuals, this approach is more accurate in distinguishing the two cohorts than more common models. Finally, the brain regions of the identified HIV pattern concur with the HIV literature that uses traditional group analysis models.

    View details for PubMedID 30138676

  • Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection COMPUTERIZED MEDICAL IMAGING AND GRAPHICS Liu, L., Wang, Q., Adeli, E., Zhang, L., Zhang, H., Shen, D. 2018; 67: 21–29

    Abstract

    Parkinson's disease (PD) is a neurodegenerative disorder that progressively hampers the brain functions and leads to various movement and non-motor symptoms. However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly demanded, so that the corresponding treatment can be implemented more appropriately. In this paper, we focus on finding the most discriminative features from different brain regions in PD through T1-weighted MR images, which can help the subsequent PD diagnosis. Specifically, we proposed a novel iterative canonical correlation analysis (ICCA) feature selection method, aiming at exploiting MR images in a more comprehensive manner and fusing features of different types into a common space. To state succinctly, we first extract the feature vectors from the gray matter and the white matter tissues separately, represented as insights of two different anatomical feature spaces for the subject's brain. The ICCA feature selection method aims at iteratively finding the optimal feature subset from two sets of features that have inherent high correlation with each other. In experiments we have conducted thorough investigations on the optimal feature set extracted by our ICCA method. We also demonstrate that using the proposed feature selection method, the PD diagnosis performance is further improved, and also outperforms many state-of-the-art methods.

    View details for DOI 10.1016/j.compmedimag.2018.04.002

    View details for Web of Science ID 000447358800003

    View details for PubMedID 29702348

  • Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism HUMAN BRAIN MAPPING Wang, L., Li, G., Adeli, E., Liu, M., Wu, Z., Meng, Y., Lin, W., Shen, D. 2018; 39 (6): 2609–23

    Abstract

    Tissue segmentation of infant brain MRIs with risk of autism is critically important for characterizing early brain development and identifying biomarkers. However, it is challenging due to low tissue contrast caused by inherent ongoing myelination and maturation. In particular, at around 6 months of age, the voxel intensities in both gray matter and white matter are within similar ranges, thus leading to the lowest image contrast in the first postnatal year. Previous studies typically employed intensity images and tentatively estimated tissue probabilities to train a sequence of classifiers for tissue segmentation. However, the important prior knowledge of brain anatomy is largely ignored during the segmentation. Consequently, the segmentation accuracy is still limited and topological errors frequently exist, which will significantly degrade the performance of subsequent analyses. Although topological errors could be partially handled by retrospective topological correction methods, their results may still be anatomically incorrect. To address these challenges, in this article, we propose an anatomy-guided joint tissue segmentation and topological correction framework for isointense infant MRI. Particularly, we adopt a signed distance map with respect to the outer cortical surface as anatomical prior knowledge, and incorporate such prior information into the proposed framework to guide segmentation in ambiguous regions. Experimental results on the subjects acquired from National Database for Autism Research demonstrate the effectiveness to topological errors and also some levels of robustness to motion. Comparisons with the state-of-the-art methods further demonstrate the advantages of the proposed method in terms of both segmentation accuracy and topological correctness.

    View details for DOI 10.1002/hbm.24027

    View details for Web of Science ID 000438015400025

    View details for PubMedID 29516625

    View details for PubMedCentralID PMC5951769

  • Conversion and time-to-conversion predictions of mild cognitive impairment using low-rank affinity pursuit denoising and matrix completion MEDICAL IMAGE ANALYSIS Thung, K., Yap, P., Adeli, E., Lee, S., Shen, D., Alzheimers Dis Neuroimaging Init 2018; 45: 68–82

    Abstract

    In this paper, we aim to predict conversion and time-to-conversion of mild cognitive impairment (MCI) patients using multi-modal neuroimaging data and clinical data, via cross-sectional and longitudinal studies. However, such data are often heterogeneous, high-dimensional, noisy, and incomplete. We thus propose a framework that includes sparse feature selection, low-rank affinity pursuit denoising (LRAD), and low-rank matrix completion (LRMC) in this study. Specifically, we first use sparse linear regressions to remove unrelated features. Then, considering the heterogeneity of the MCI data, which can be assumed as a union of multiple subspaces, we propose to use a low rank subspace method (i.e., LRAD) to denoise the data. Finally, we employ LRMC algorithm with three data fitting terms and one inequality constraint for joint conversion and time-to-conversion predictions. Our framework aims to answer a very important but yet rarely explored question in AD study, i.e., when will the MCI convert to AD? This is different from survival analysis, which provides the probabilities of conversion at different time points that are mainly used for global analysis, while our time-to-conversion prediction is for each individual subject. Evaluations using the ADNI dataset indicate that our method outperforms conventional LRMC and other state-of-the-art methods. Our method achieves a maximal pMCI classification accuracy of 84% and time prediction correlation of 0.665.

    View details for DOI 10.1016/j.media.2018.01.002

    View details for Web of Science ID 000427664400006

    View details for PubMedID 29414437

    View details for PubMedCentralID PMC6892173

  • INFANT BRAIN DEVELOPMENT PREDICTION WITH LATENT PARTIAL MULTI-VIEW REPRESENTATION LEARNING. Proceedings. IEEE International Symposium on Biomedical Imaging Zhang, C. n., Adeli, E. n., Wu, Z. n., Li, G. n., Lin, W. n., Shen, D. n. 2018; 2018: 1048–51

    Abstract

    The early postnatal period witnesses rapid and dynamic brain development. Understanding the cognitive development patterns can help identify various disorders at early ages of life and is essential for the health and well-being of children. This inspires us to investigate the relation between cognitive ability and the cerebral cortex by exploiting brain images in a longitudinal study. Specifically, we aim to predict the infant brain development status based on the morphological features of the cerebral cortex. For this goal, we introduce a multi-view multi-task learning approach to dexterously explore complementary information from different time points and handle the missing data simultaneously. Specifically, we establish a novel model termed as Latent Partial Multi-view Representation Learning. The approach regards data of different time points as different views, and constructs a latent representation to capture the complementary underlying information from different and even incomplete time points. It uncovers the latent representation that can be jointly used to learn the prediction model. This formulation elegantly explores the complementarity, effectively reduces the redundancy of different views, and improves the accuracy of prediction. The minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on real data validate the proposed method.

    View details for PubMedID 30464798

    View details for PubMedCentralID PMC6242279

  • Multi-Label Transduction for Identifying Disease Comorbidity Patterns. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Adeli, E. n., Kwon, D. n., Pohl, K. M. 2018; 11072: 575–83

    Abstract

    Study of the untoward effects associated with the comorbidity of multiple diseases on brain morphology requires identifying differences across multiple diagnostic groupings. To identify such effects and differentiate between groups of patients and normal subjects, conventional methods often compare each patient group with healthy subjects using binary or multi-class classifiers. However, testing inferences across multiple diagnostic groupings of complex disorders commonly yield inconclusive or conflicting findings when the classifier is confined to modeling two cohorts at a time or considers class labels mutually-exclusive (as in multi-class classifiers). These shortcomings are potentially caused by the difficulties associated with modeling compounding factors of diseases with these approaches. Multi-label classifiers, on the other hand, can appropriately model disease comorbidity, as each subject can be assigned to two or more labels. In this paper, we propose a multi-label transductive (MLT) method based on low-rank matrix completion that is able not only to classify the data into multiple labels but also to identify patterns from MRI data unique to each cohort. To evaluate the method, we use a dataset containing individuals with Alcohol Use Disorder (AUD) and human immunodeficiency virus (HIV) infection (specifically 244 healthy controls, 227 AUD, 70 HIV, and 61 AUD+HIV). On this dataset, our proposed method is more accurate in correctly labeling subjects than common approaches. Furthermore, our method identifies patterns specific to each disease and AUD+HIV comorbidity that shows that the comorbidity is characterized by a compounding effect of AUD and HIV infection.

    View details for DOI 10.1007/978-3-030-00931-1_66

    View details for PubMedID 33688637

    View details for PubMedCentralID PMC7938692

  • End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification. Machine learning in medical imaging. MLMI (Workshop) Esmaeilzadeh, S. n., Belivanis, D. I., Pohl, K. M., Adeli, E. n. 2018; 11046: 337–45

    Abstract

    As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods.

    View details for DOI 10.1007/978-3-030-00919-9_39

    View details for PubMedID 32832936

    View details for PubMedCentralID PMC7440044

  • Landmark-based deep multi-instance learning for brain disease diagnosis MEDICAL IMAGE ANALYSIS Liu, M., Zhang, J., Adeli, E., Shen, D. 2018; 43: 157–68

    Abstract

    In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches.

    View details for DOI 10.1016/j.media.2017.10.005

    View details for Web of Science ID 000418627400012

    View details for PubMedID 29107865

    View details for PubMedCentralID PMC6203325

  • End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification Esmaeilzadeh, S., Belivanis, D., Pohl, K. M., Adeli, E., Shi, Y., Suk, H. I., Liu, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 337–45

    View details for DOI 10.1007/978-3-030-00919-9_39

    View details for Web of Science ID 000477767800039

  • Multi-label Transduction for Identifying Disease Comorbidity Patterns Adeli, E., Kwon, D., Pohl, K. M., Frangi, A. F., Schnabel, J. A., Davatzikos, C., AlberolaLopez, C., Fichtinger, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 575–83

    View details for DOI 10.1007/978-3-030-00931-1_66

    View details for Web of Science ID 000477769700066

  • Multi-Layer Multi-View Classification for Alzheimer's Disease Diagnosis Zhang, C., Adeli, E., Zhou, T., Chen, X., Shen, D., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2018: 4406–13

    Abstract

    In this paper, we propose a novel multi-view learning method for Alzheimer's Disease (AD) diagnosis, using neuroimaging and genetics data. Generally, there are several major challenges associated with traditional classification methods on multi-source imaging and genetics data. First, the correlation between the extracted imaging features and class labels is generally complex, which often makes the traditional linear models ineffective. Second, medical data may be collected from different sources (i.e., multiple modalities of neuroimaging data, clinical scores or genetics measurements), therefore, how to effectively exploit the complementarity among multiple views is of great importance. In this paper, we propose a Multi-Layer Multi-View Classification (ML-MVC) approach, which regards the multi-view input as the first layer, and constructs a latent representation to explore the complex correlation between the features and class labels. This captures the high-order complementarity among different views, as we exploit the underlying information with a low-rank tensor regularization. Intrinsically, our formulation elegantly explores the nonlinear correlation together with complementarity among different views, and thus improves the accuracy of classification. Finally, the minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on Alzheimers Disease Neuroimaging Initiative (ADNI) data sets validate the effectiveness of our proposed method.

    View details for Web of Science ID 000485488904061

    View details for PubMedID 30416868

    View details for PubMedCentralID PMC6223635

  • Fine-Grained Segmentation Using Hierarchical Dilated Neural Networks Zhou, S., Nie, D., Adeli, E., Gao, Y., Wang, L., Yin, J., Shen, D., Frangi, A. F., Schnabel, J. A., Davatzikos, C., AlberolaLopez, C., Fichtinger, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 488–96

    View details for DOI 10.1007/978-3-030-00937-3_56

    View details for Web of Science ID 000477769100056

  • Predictive Modeling of Longitudinal Data for Alzheimer's Disease Diagnosis Using RNNs Aghili, M., Tabarestani, S., Adjouadi, M., Adeli, E., Rekik, Unal, G., Adeli, E., Park, S. H. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 112–19

    View details for DOI 10.1007/978-3-030-00320-3_14

    View details for Web of Science ID 000477923900014

  • Joint Sparse and Low-Rank Regularized MultiTask Multi-Linear Regression for Prediction of Infant Brain Development with Incomplete Data. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Adeli, E., Meng, Y., Li, G., Lin, W., Shen, D. 2017; 10433: 40–48

    Abstract

    Studies involving dynamic infant brain development has received increasing attention in the past few years. For such studies, a complete longitudinal dataset is often required to precisely chart the early brain developmental trajectories. Whereas, in practice, we often face missing data at different time point(s) for different subjects. In this paper, we propose a new method for prediction of infant brain development scores at future time points based on longitudinal imaging measures at early time points with possible missing data. We treat this as a multi-dimensional regression problem, for predicting multiple brain development scores (multi-task) from multiple previous time points (multi-linear). To solve this problem, we propose an objective function with a joint ℓ1 and low-rank regularization on the mapping weight tensor, to enforce feature selection, while preserving the structural information from multiple dimensions. Also, based on the bag-of-words model, we propose to extract features from longitudinal imaging data. The experimental results reveal that we can effectively predict the brain development scores assessed at the age of four years, using the imaging data as early as two years of age.

    View details for DOI 10.1007/978-3-319-66182-7_5

    View details for PubMedID 30159549

  • Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Data. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Zhu, X., Thung, K., Adeli, E., Zhang, Y., Shen, D. 2017; 10435: 72–80

    Abstract

    It is challenging to use incomplete multimodality data for Alzheimer's Disease (AD) diagnosis. The current methods to address this challenge, such as low-rank matrix completion (i.e., imputing the missing values and unknown labels simultaneously) and multi-task learning (i.e., defining one regression task for each combination of modalities and then learning them jointly), are unable to model the complex data-to-label relationship in AD diagnosis and also ignore the heterogeneity among the modalities. In light of this, we propose a new Maximum Mean Discrepancy (MMD) based Multiple Kernel Learning (MKL) method for AD diagnosis using incomplete multimodality data. Specifically, we map all the samples from different modalities into a Reproducing Kernel Hilbert Space (RKHS), by devising a new MMD algorithm. The proposed MMD method incorporates data distribution matching, pair-wise sample matching and feature selection in an unified formulation, thus alleviating the modality heterogeneity issue and making all the samples comparable to share a common classifier in the RKHS. The resulting classifier obviously captures the nonlinear data-to-label relationship. We have tested our method using MRI and PET data from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset for AD diagnosis. The experimental results show that our method outperforms other methods.

    View details for DOI 10.1007/978-3-319-66179-7_9

    View details for PubMedID 29392246

  • Deep Multi-Task Multi-Channel Learning for Joint Classification and Regression of Brain Status. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Liu, M., Zhang, J., Adeli, E., Shen, D. 2017; 10435: 3–11

    Abstract

    Jointly identifying brain diseases and predicting clinical scores have attracted increasing attention in the domain of computer-aided diagnosis using magnetic resonance imaging (MRI) data, since these two tasks are highly correlated. Although several joint learning models have been developed, most existing methods focus on using human-engineered features extracted from MRI data. Due to the possible heterogeneous property between human-engineered features and subsequent classification/regression models, those methods may lead to sub-optimal learning performance. In this paper, we propose a deep multi-task multi-channel learning (DM2L) framework for simultaneous classification and regression for brain disease diagnosis, using MRI data and personal information (i.e., age, gender, and education level) of subjects. Specifically, we first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then extract multiple image patches around these detected landmarks. A deep multi-task multi-channel convolutional neural network is then developed for joint disease classification and clinical score regression. We train our model on a large multi-center cohort (i.e., ADNI-1) and test it on an independent cohort (i.e., ADNI-2). Experimental results demonstrate that DM2L is superior to the state-of-the-art approaches in brain diasease diagnosis.

    View details for DOI 10.1007/978-3-319-66179-7_1

    View details for PubMedID 29756129

  • Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning MEDICAL IMAGE ANALYSIS Wang, Z., Zhu, X., Adeli, E., Zhu, Y., Nie, F., Munsell, B., Wu, G., ADNI PPMI 2017; 39: 218–30

    Abstract

    Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is based on similarities in voxel intensity values in the feature domain, which can then be used to propagate the known phenotype data (i.e., clinical scores and labels) from the training data to the testing data in the label domain. However, this type of graph is exclusively learned in the feature domain, and primarily due to outliers in the observed features, may not be optimal for label propagation in the label domain. To address this limitation, a progressive GTL (pGTL) method is proposed that gradually finds an intrinsic data representation that more accurately aligns imaging features with the phenotype data. In general, optimal feature-to-phenotype alignment is achieved using an iterative approach that: (1) refines inter-subject relationships observed in the feature domain by using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined inter-subject relationships, and (3) verifies the intrinsic data representation on the training data to guarantee an optimal classification when applied to testing data. Additionally, the iterative approach is extended to multi-modal imaging data to further improve pGTL classification accuracy. Using Alzheimer's disease and Parkinson's disease study data, the classification accuracy of the proposed pGTL method is compared to several state-of-the-art classification methods, and the results show pGTL can more accurately identify subjects, even at different progression stages, in these two study data sets.

    View details for DOI 10.1016/j.media.2017.05.003

    View details for Web of Science ID 000404200900016

    View details for PubMedID 28551556

    View details for PubMedCentralID PMC5901767

  • A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer's Disease Diagnosis SCIENTIFIC REPORTS An, L., Adeli, E., Liu, M., Zhang, J., Lee, S., Shen, D. 2017; 7: 45269

    Abstract

    Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer's disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.

    View details for DOI 10.1038/srep45269

    View details for Web of Science ID 000397815500001

    View details for PubMedID 28358032

    View details for PubMedCentralID PMC5372170

  • Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson's Disease SCIENTIFIC REPORTS Adeli, E., Wu, G., Saghafi, B., An, L., Shi, F., Shen, D. 2017; 7: 41069

    Abstract

    Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson's disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

    View details for DOI 10.1038/srep41069

    View details for Web of Science ID 000392663200001

    View details for PubMedID 28120883

    View details for PubMedCentralID PMC5264393

  • Structured Prediction with Short/Long-Range Dependencies for Human Activity Recognition from Depth Skeleton Data Arzani, M. M., Fathy, M., Aghajan, H., Azirani, A. A., Raahemifar, K., Adeli, E., Bicchi, A., Okamura, A. IEEE. 2017: 560–67

    View details for Web of Science ID 000426978200079

  • Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity Network Jia, X., Zhang, H., Adeli, E., Shen, D., Wu, G., Laurienti, P., Bonilha, L., Munsell, B. C. SPRINGER INTERNATIONAL PUBLISHING AG. 2017: 17–24

    Abstract

    Based on the neuroimaging data from a large set of acquired brain injury patients, we investigate the feasibility of using machine learning for automatic prediction of individual consciousness level. Rather than using the traditional Pearson's correlation-based brain functional network, which measures only the simple temporal synchronization of the BOLD signals from each pair of brain regions, we construct a high-order brain functional network that is capable of characterizing topographical information-based high-level functional associations among brain regions. In such a high-order brain network, each node represents the community of a brain region, described by a set of this region's low-order functional associations with other brain regions, and each edge characterizes topographical similarity between a pair of such communities. Experimental results show that the high-order brain functional network enables a significant better classification for consciousness level and recovery outcome prediction.

    View details for DOI 10.1007/978-3-319-67159-8_3

    View details for Web of Science ID 000463626800003

    View details for PubMedID 30345427

    View details for PubMedCentralID PMC6193499

  • Deep Relative Attributes Souri, Y., Noury, E., Adeli, E., Lai, S. H., Lepetit, Nishino, K., Sato, Y. SPRINGER INTERNATIONAL PUBLISHING AG. 2017: 118–33

    View details for DOI 10.1007/978-3-319-54193-8_8

    View details for Web of Science ID 000426209200008

  • Joint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data NEUROIMAGE Adeli, E., Shi, F., An, L., Wee, C., Wu, G., Wang, T., Shen, D. 2016; 141: 206–19

    Abstract

    Parkinson's disease (PD) is an overwhelming neurodegenerative disorder caused by deterioration of a neurotransmitter, known as dopamine. Lack of this chemical messenger impairs several brain regions and yields various motor and non-motor symptoms. Incidence of PD is predicted to double in the next two decades, which urges more research to focus on its early diagnosis and treatment. In this paper, we propose an approach to diagnose PD using magnetic resonance imaging (MRI) data. Specifically, we first introduce a joint feature-sample selection (JFSS) method for selecting an optimal subset of samples and features, to learn a reliable diagnosis model. The proposed JFSS model effectively discards poor samples and irrelevant features. As a result, the selected features play an important role in PD characterization, which will help identify the most relevant and critical imaging biomarkers for PD. Then, a robust classification framework is proposed to simultaneously de-noise the selected subset of features and samples, and learn a classification model. Our model can also de-noise testing samples based on the cleaned training data. Unlike many previous works that perform de-noising in an unsupervised manner, we perform supervised de-noising for both training and testing data, thus boosting the diagnostic accuracy. Experimental results on both synthetic and publicly available PD datasets show promising results. To evaluate the proposed method, we use the popular Parkinson's progression markers initiative (PPMI) database. Our results indicate that the proposed method can differentiate between PD and normal control (NC), and outperforms the competing methods by a relatively large margin. It is noteworthy to mention that our proposed framework can also be used for diagnosis of other brain disorders. To show this, we have also conducted experiments on the widely-used ADNI database. The obtained results indicate that our proposed method can identify the imaging biomarkers and diagnose the disease with favorable accuracies compared to the baseline methods.

    View details for DOI 10.1016/j.neuroimage.2016.05.054

    View details for Web of Science ID 000384074500018

    View details for PubMedID 27296013

    View details for PubMedCentralID PMC5866718

  • Feature Selection Based on Iterative Canonical Correlation Analysis for Automatic Diagnosis of Parkinson's Disease. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Liu, L., Wang, Q., Adeli, E., Zhang, L., Zhang, H., Shen, D. 2016; 9901: 1–8

    Abstract

    Parkinson's disease (PD) is a major progressive neurodegenerative disorder. Accurate diagnosis of PD is crucial to control the symptoms appropriately. However, its clinical diagnosis mostly relies on the subjective judgment of physicians and the clinical symptoms that often appear late. Recent neuroimaging techniques, along with machine learning methods, provide alternative solutions for PD screening. In this paper, we propose a novel feature selection technique, based on iterative canonical correlation analysis (ICCA), to investigate the roles of different brain regions in PD through T1-weighted MR images. First of all, gray matter and white matter tissue volumes in brain regions of interest are extracted as two feature vectors. Then, a small group of significant features were selected using the iterative structure of our proposed ICCA framework from both feature vectors. Finally, the selected features are used to build a robust classifier for automatic diagnosis of PD. Experimental results show that the proposed feature selection method results in better diagnosis accuracy, compared to the baseline and state-of-the-art methods.

    View details for DOI 10.1007/978-3-319-46723-8_1

    View details for PubMedID 28593202

  • Progressive Graph-Based Transductive Learning for Multi-modal Classification of Brain Disorder Disease. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Wang, Z., Zhu, X., Adeli, E., Zhu, Y., Zu, C., Nie, F., Shen, D., Wu, G. 2016; 9900: 291–99

    Abstract

    Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis, especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e., extracted from imaging data) in the feature domain, and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However, such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain. This may eventually undermine the classification accuracy. To address this issue, we propose a progressive GTL (pGTL) method to progressively find an intrinsic data representation. To achieve this, our pGTL method iteratively (1) refines the subject-wise relationships observed in the feature domain using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined subject-wise relationships, and (3) verifies the intrinsic data representation on the training data, in order to guarantee an optimal classification on the new testing data. Furthermore, we extend our pGTL to incorporate multi-modal imaging data, to improve the classification accuracy and robustness as multi-modal imaging data can provide complementary information. Promising classification results in identifying Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal Control (NC) subjects are achieved using MRI and PET data.

    View details for DOI 10.1007/978-3-319-46720-7_34

    View details for PubMedID 28386606

  • 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Nie, D., Zhang, H., Adeli, E., Liu, L., Shen, D. 2016; 9901: 212–20

    Abstract

    High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50% patients in 1-2 years. Thus, accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional survival prediction generally utilizes clinical information and limited handcrafted features from magnetic resonance images (MRI), which is often time consuming, laborious and subjective. In this paper, we propose using deep learning frameworks to automatically extract features from multi-modal preoperative brain images (i.e., T1 MRI, fMRI and DTI) of high-grade glioma patients. Specifically, we adopt 3D convolutional neural networks (CNNs) and also propose a new network architecture for using multi-channel data and learning supervised features. Along with the pivotal clinical features, we finally train a support vector machine to predict if the patient has a long or short overall survival (OS) time. Experimental results demonstrate that our methods can achieve an accuracy as high as 89.9% We also find that the learned features from fMRI and DTI play more important roles in accurately predicting the OS time, which provides valuable insights into functional neuro-oncological applications.

    View details for DOI 10.1007/978-3-319-46723-8_25

    View details for PubMedID 28149967

  • Semi-supervised Hierarchical Multimodal Feature and Sample Selection for Alzheimer's Disease Diagnosis. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention An, L., Adeli, E., Liu, M., Zhang, J., Shen, D. 2016; 9901: 79–87

    Abstract

    Alzheimer's disease (AD) is a progressive neurodegenerative disease that impairs a patient's memory and other important mental functions. In this paper, we leverage the mutually informative and complementary features from both structural magnetic resonance imaging (MRI) and single nucleotide polymorphism (SNP) for improving the diagnosis. Due to the feature redundancy and sample outliers, direct use of all training data may lead to suboptimal performance in classification. In addition, as redundant features are involved, the most discriminative feature subset may not be identified in a single step, as commonly done in most existing feature selection approaches. Therefore, we formulate a hierarchical multimodal feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps. To positively guide the data manifold preservation, we utilize both labeled and unlabeled data in the learning process, making our method semi-supervised. The finally selected features and samples are then used to train support vector machine (SVM) based classification models. Our method is evaluated on 702 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and the superior classification results in AD related diagnosis demonstrate the effectiveness of our approach as compared to other methods.

    View details for DOI 10.1007/978-3-319-46723-8_10

    View details for PubMedID 30101233

  • Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Liu, M., Zhang, D., Adeli, E., Shen, D. 2016; 63 (7): 1473–82

    Abstract

    Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods.

    View details for DOI 10.1109/TBME.2015.2496233

    View details for Web of Science ID 000380323800013

    View details for PubMedID 26540666

    View details for PubMedCentralID PMC4851920

  • Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation IEEE TRANSACTIONS ON IMAGE PROCESSING An, L., Zhang, P., Adeli, E., Wang, Y., Ma, G., Shi, F., Lalush, D. S., Lin, W., Shen, D. 2016; 25 (7): 3303–15

    Abstract

    Positron emission tomography (PET) images are widely used in many clinical applications, such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both S-PET and low-dose PET data into a common space and then performing patch-based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level canonical correlation analysis scheme to solve this problem. In particular, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. In addition, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain data sets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.

    View details for DOI 10.1109/TIP.2016.2567072

    View details for Web of Science ID 000377371700002

    View details for PubMedID 27187957

    View details for PubMedCentralID PMC5106345

  • Stability-Weighted Matrix Completion of Incomplete Multi-modal Data for Disease Diagnosis. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Thung, K., Adeli, E., Yap, P., Shen, D. 2016; 9901: 88–96

    Abstract

    Effective utilization of heterogeneous multi-modal data for Alzheimer's Disease (AD) diagnosis and prognosis has always been hampered by incomplete data. One method to deal with this is low-rank matrix completion (LRMC), which simultaneous imputes missing data features and target values of interest. Although LRMC yields reasonable results, it implicitly weights features from all the modalities equally, ignoring the differences in discriminative power of features from different modalities. In this paper, we propose stability-weighted LRMC (swLRMC), an LRMC improvement that weights features and modalities according to their importance and reliability. We introduce a method, called stability weighting, to utilize subsampling techniques and outcomes from a range of hyper-parameters of sparse feature learning to obtain a stable set of weights. Incorporating these weights into LRMC, swLRMC can better account for differences in features and modalities for improving diagnosis. Experimental results confirm that the proposed method outperforms the conventional LRMC, feature-selection based LRMC, and other state-of-the-art methods.

    View details for DOI 10.1007/978-3-319-46723-8_11

    View details for PubMedID 28286884

  • Joint Feature-Sample Selection and Robust Classification for Parkinson's Disease Diagnosis Adeli-Mosabbeb, E., Wee, C., An, L., Shi, F., Shen, D., Menze, B., Langs, G., Montillo, A., Kelm, M., Muller, H., Zhang, S., Cai, W., Metaxas, D. SPRINGER INTERNATIONAL PUBLISHING AG. 2016: 127–36

    View details for DOI 10.1007/978-3-319-42016-5_12

    View details for Web of Science ID 000389404000012

  • Relationship Induced Multi-atlas Learning for Alzheimer's Disease Diagnosis Liu, M., Zhang, D., Adeli-Mosabbeb, E., Shen, D., Menze, B., Langs, G., Montillo, A., Kelm, M., Muller, H., Zhang, S., Cai, W., Metaxas, D. SPRINGER INTERNATIONAL PUBLISHING AG. 2016: 24–33

    View details for DOI 10.1007/978-3-319-42016-5_3

    View details for Web of Science ID 000389404000003

Latest information on COVID-19