Bio

Bio


I received my B. Tech(Hons) with a major in Electronics and Electrical Communication Engineering and a minor in Computer Science and Engineering from Indian Institute of Technology (IIT), Kharagpur in 2010. In 2016, I obtained a Ph.D in Electrical and Computer Engineering at the University of Maryland, College Park under the guidance of Prof. Sennur Ulukus. From January to December 2017, I was a postdoctoral researcher in the Electrical Engineering department at Stanford University with Prof. Tsachy Weissman and Prof. Ayfer Ozgur. From January 2018, I joined the Gevaertlab at BMIR in the Stanford School of Medicine where I am currently pursuing research into the application of machine learning and deep learning to uncover the interplay between biomedical imaging and genomics, as they relate to cancer research.

Professional Education


  • Doctor of Philosophy, University of Maryland College Park (2016)
  • Bachelor of Technology, Indian Institute of Technology, Kharagpur (2010)

Publications

All Publications


  • CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma. Radiology. Imaging cancer Mukherjee, P., Cintra, M., Huang, C., Zhou, M., Zhu, S., Colevas, A. D., Fischbein, N., Gevaert, O. 2020; 2 (3): e190039

    Abstract

    Purpose: To determine the performance of CT-based radiomic features for noninvasive prediction of histopathologic features of tumor grade, extracapsular spread, perineural invasion, lymphovascular invasion, and human papillomavirus status in head and neck squamous cell carcinoma (HNSCC).Materials and Methods: In this retrospective study, which was approved by the local institutional ethics committee, CT images and clinical data from patients with pathologically proven HNSCC from The Cancer Genome Atlas (n = 113) and an institutional test cohort (n = 71) were analyzed. A machine learning model was trained with 2131 extracted radiomic features to predict tumor histopathologic characteristics. In the model, principal component analysis was used for dimensionality reduction, and regularized regression was used for classification.Results: The trained radiomic model demonstrated moderate capability of predicting HNSCC features. In the training cohort and the test cohort, the model achieved a mean area under the receiver operating characteristic curve (AUC) of 0.75 (95% confidence interval [CI]: 0.68, 0.81) and 0.66 (95% CI: 0.45, 0.84), respectively, for tumor grade; a mean AUC of 0.64 (95% CI: 0.55, 0.62) and 0.70 (95% CI: 0.47, 0.89), respectively, for perineural invasion; a mean AUC of 0.69 (95% CI: 0.56, 0.81) and 0.65 (95% CI: 0.38, 0.87), respectively, for lymphovascular invasion; a mean AUC of 0.77 (95% CI: 0.65, 0.88) and 0.67 (95% CI: 0.15, 0.80), respectively, for extracapsular spread; and a mean AUC of 0.71 (95% CI: 0.29, 1.0) and 0.80 (95% CI: 0.65, 0.92), respectively, for human papillomavirus status.Conclusion: Radiomic CT models have the potential to predict characteristics typically identified on pathologic assessment of HNSCC.Supplemental material is available for this article.© RSNA, 2020.

    View details for DOI 10.1148/rycan.2020190039

    View details for PubMedID 32550599

  • A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets Nature Machine Intelligence Mukherjee, P., Zhou, M., Lee, E., Schicht, A., Balagurunathan, Y., Napel, S., Gillies, R., Wong, S., Thieme, A., Leung, A., Gevaert, O. 2020; 2 (5): 274–282
  • Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Bulens, P., Couwenberg, A., Intven, M., Debucquoy, A., Vandecaveye, V., Van Cutsem, E., D'Hoore, A., Wolthuis, A., Mukherjee, P., Gevaert, O., Haustermans, K. 2019

    Abstract

    BACKGROUND: In well-responding patients to chemoradiotherapy for locally advanced rectal cancer (LARC), a watch-and-wait strategy can be considered. To implement organ-sparing strategies, accurate patient selection is needed. We investigate the use of MRI-based radiomics models to predict tumor response to improve patient selection.MATERIALS AND METHODS: Models were developed in a cohort of 70 patients and validated in an external cohort of 55 patients. Patients received chemoradiation followed by surgery and underwent T2-weighted and diffusion-weighted MRI (DW-MRI) before and after chemoradiation. The outcome measure was (near-)complete pathological tumor response (ypT0-1N0). Tumor segmentation was done on T2-images and transferred to b800-images and ADC maps, after which quantitative and four semantic features were extracted. We combined features using principal component analysis and built models using LASSO regression analysis. The best models based on precision and performance were selected for validation.RESULTS: 21/70 patients (30%) achieved ypT0-1N0 in the development cohort versus 13/55 patients (24%) in the validation cohort. Three models (t2_dwi_pre_post, semantic_dwi_adc_pre, semantic_dwi_post) were identified with an area-under-the-curve (AUC) of 0.83 (95% CI 0.70-0.95), 0.86 (95% CI 0.75-0.98) and 0.84 (95% CI 0.75-0.94) respectively. Two models (t2_dwi_pre_post, semantic_dwi_post) validated well in the external cohort with AUCs of 0.83 (95% CI 0.70-0.95) and 0.86 (95% CI 0.76-0.97). These models however did not outperform a previously established four-feature semantic model.CONCLUSION: Prediction models based on MRI radiomics non-invasively predict tumor response after chemoradiation for rectal cancer and can be used as an additional tool to identify patients eligible for an organ-preserving treatment.

    View details for DOI 10.1016/j.radonc.2019.07.033

    View details for PubMedID 31431368

  • Secure Degrees of Freedom of the Multiple Access Wiretap Channel With Multiple Antennas IEEE TRANSACTIONS ON INFORMATION THEORY Mukherjee, P., Ulukus, S. 2018; 64 (3): 2093–2103
  • Distributed Statistical Estimation of High-Dimensional and Nonparametric Distributions Han, Y., Mukherjee, P., Ozgur, A., Weissman, T., IEEE IEEE. 2018: 506–10
  • Secrecy in MIMO Networks With No Eavesdropper CSIT Mukherjee, P., Ulukus, S. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2017: 4382–91
  • Secure Degrees of Freedom Region of the Two-User MISO Broadcast Channel With Alternating CSIT IEEE TRANSACTIONS ON INFORMATION THEORY Mukherjee, P., Tandon, R., Ulukus, S. 2017; 63 (6): 3823–53
  • Secure Degrees of Freedom of One-Hop Wireless Networks With No Eavesdropper CSIT Mukherjee, P., Xie, J., Ulukus, S. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2017: 1898–1922
  • Covert Bits Through Queues Mukherjee, P., Ulukus, S., IEEE IEEE. 2016: 626–30
  • Real Interference Alignment for the MIMO Multiple Access Wiretap Channel Mukherjee, P., Ulukus, S., IEEE IEEE. 2016
  • Real Interference Alignment for Vector Channels Mukherjee, P., Ulukus, S., IEEE IEEE. 2016: 1476–80
  • MIMO One Hop Networks with No Eve CSIT Mukherjee, P., Ulukus, S., IEEE IEEE. 2016: 894–901
  • Secure Degrees of Freedom of MIMO Rayleigh Block Fading Wiretap Channels With No CSI Anywhere IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Liu, T., Mukherjee, P., Ulukus, S., Lin, S., Hong, Y. 2015; 14 (5): 2655–69
  • Secure Degrees of Freedom of the Interference Channel with No Eavesdropper CSI Mukherjee, P., Ulukus, S., IEEE IEEE. 2015: 317–21
  • Secrecy for MISO Broadcast Channels via Alternating CSIT Mukherjee, P., Tandon, R., Ulukus, S., IEEE IEEE. 2015: 4157–62
  • Secrecy for MISO Broadcast Channels with Heterogeneous CSIT Mukherjee, P., Tandon, R., Ulukus, S., IEEE IEEE. 2015: 1966–70
  • Secure Degrees of Freedom of the Multiple Access Wiretap Channel with No Eavesdropper CSI Mukherjee, P., Ulukus, S., IEEE IEEE. 2015: 2311–15
  • MISO Broadcast Channels with Confidential Messages and Alternating CSIT Mukherjee, P., Tandon, R., Ulukus, S., IEEE IEEE. 2014: 216–20
  • Secure DoF of MIMO Rayleigh Block Fading Wiretap Channels with No CSI Anywhere Liu, T., Mukherjee, P., Ulukus, S., Lin, S., Hong, Y., Jamalipour, A., Deng, D. J. IEEE. 2014: 1959–64
  • Even Symmetric Parallel Linear Deterministic Interference Channels are Inseparable Mukherjee, P., Tandon, R., Ulukus, S., IEEE IEEE. 2013: 1106–13
  • Fading Wiretap Channel with No CSI Anywhere Mukherjee, P., Ulukus, S., IEEE IEEE. 2013: 1347–51
  • A SPT Treatment to the Realization of the Sign-LMS Based Adaptive Filters IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS Choudhary, S., Mukherjee, P., Chakraborty, M., Rath, S. 2012; 59 (9): 2025–33
  • A SPT treatment to the Bit Serial Realization of the Sign-LMS based Adaptive Filter Choudhary, S., Mukherjee, P., Chakraborty, M., IEEE IEEE. 2010: 2678–81

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