Speaker Profile

Ph.D., Professor of Biomedical Engineering, Case Western Reserve University

Madabhushi’s team at the Center for Computational Imaging and Personalized Diagnostics (CCIPD) is developing and applying novel AI and computational imaging approaches for the diagnosis, prognosis and prediction of therapy response for a variety of diseases including oncology, cardiovascular, kidney and eye disease. Madabhushi has more than 100 patents and authored over 400 journal and conference papers. Madabhushi is a fellow of the American Institute of Medical and Biomedical Engineering and the Institute of Electrical and Electronics Engineers. Madabhushi’s work on “smart computers for identifying lung cancer patients who will benefit from chemotherapy” was ranked in the top 10 medical breakthroughs of 2018 by Prevention Magazine. In 2019, Nature Magazine called him out as one of five scientists pursuing truly offbeat and innovative approaches in cancer research. His work has been awarded >$60 million in grant funding, has co-founded two companies, and has had 15 of his technologies licensed.

Prognostic And Predictive Radiomics And Pathomics For Precision Oncology
Our group has been pioneering computerized feature analysis methods for extracting subvisual attributes for characterizing disease appearance and behavior on radiographic (radiomics) and digitized pathology images (pathomics). In this talk I will discuss how these radiomic and pathomic approaches can be applied to predicting disease outcome, recurrence, progression and response to therapy in the context of prostate, brain, rectal, oropharyngeal, and lung cancers. Additionally I will also discuss some recent work on looking at use of pathomics in the context of racial health disparity and creation of more precise and tailored prognostic and treatment response prediction models.

 Session Abstract – PMWC 2020 Silicon Valley


AI-Based Radiology and Digital Pathology Applications
Sharmila Majumdar, UCSF
Matthew Lungren , Stanford
Ismail Baris Turkbey, NIH
Anant Madabhushi, Case Western Reserve University
Lawrence Schwartz, Columbia University Medical Center
Jayashree Kalpathy-Cramer, Harvard

How Data Science Will Impact and Drive Diagnostics by Combining Radiology with Pathology and Genomics

Developing and Commercializing Clinically Relevant AI-Based Imaging Solutions
Kyung Hyun Sung, UCLA
Luis Pineda, Facebook AI Research

Regulatory Considerations for AI & Machine Learning in Medical Imaging
Matthew Diamond, FDA

How Do We Enable the Next Generation of Medical Imaging for Better Diagnosis and Prognosis