12:00 PM - 01:00 PM
Stanford University School of Medicine291 Campus Dr
Stanford, CA 94305
Medical Physics Seminar - Satish Viswanath
Radiomics & AI: Designing computational imaging models for precision medicine
Webinar URL: https://stanford.zoom.us/webinar/register/WN_j1zCOA3wTUaOW1k9ue6zHQ
Dial: US: +1 650 724 9799 or +1 833 302 1536 (Toll Free)
Webinar ID: 981 4830 1798
12:00PM – 1:00PM Seminar & Discussion
Sponsored by the Radiation Oncology, Division of Medical Physics
Satish Viswanath, PhD, Assistant Professor in the Department of Biomedical Engineering at Case Western Reserve University
Dr. Viswanath is currently an Assistant Professor in the Department of Biomedical Engineering, Case Western Reserve University, with secondary appointments in Radiology and ECSE. The primary focus of his research has been developing new image analytics, radiomics, and machine learning schemes, applied to problems in computer-aided diagnosis & detection, disease characterization, as well as quantitative evaluation of response to treatment, in gastrointestinal cancers and digestive diseases.
He has authored nearly 35 peer-reviewed journal publications, over 80 conference papers & abstracts, 1 book chapter, as well as delivered over 55 invited talks and panel discussions both in the US and abroad.He has 7 issued patents in the areas of medical image analysis, computer-aided diagnosis, and pattern recognition. His research in colorectal cancers and digestive diseases has been continuously funded since 2016 through the DOD/CDMRP as well as the NIH/NCI. He is an Associate Editor for three leading medical imaging journals, as well as a Program Committee member for three major medical imaging conferences. He has served as leader for the Case Comprehensive Cancer Center Machine Learning Working Group since 2020, when he was named as one of Crain’s Business Cleveland “40 under 40”. He was most recently elected to Senior Member in the National Academy of Inventors, the IEEE, and the SPIE in 2022.
Developing artificial intelligence (AI) schemes to assist the clinician towards enabling precision medicine requires “unlocking” embedded information captured by different data modalities, in an intuitive and generalizable fashion. The research in my group focuses on developing unique AI tools that can capture biologically relevant and clinically intuitive measurements from routinely acquired imaging (MRI, CT, PET) or digitized images of tissue specimens. Further, our AI tools inform and enrich these imaging measurements with spatially resolved molecular, serum, or pathologic information. This in turn enables cross-scale association between imaging, pathology, and -omics data modalities towards building more accurate computational imaging predictors that offer improved risk stratification, disease modeling, and biological quantitation in vivo. In addition to developing approaches to ensure these models generalize tio new unseen data, we have also evaluated their repeatability across imaging parameters and reproducibility across institution- or scanner-specific variations. Problems being addressed by us include: (a) predicting response to treatment to identify optimal therapeutic pathways, as well as (b) evaluating therapeutic response to guide follow-up procedures; in the context of colorectal cancers and digestive diseases.
Video will be uploaded soon!