Speaker Profile

Ph.D., Professor, Case Western Reserve University

Biography
Dr. Anant Madabhushi is Director of the Center for Computational Imaging and Personalized Diagnostics (CCIPD) and Donnell Institute Professor, Department of Biomedical Engineering at Case Western Reserve University. He is a Research Health Scientist at the Louis Stokes, Cleveland Veterans Administration Medical Center. Dr. Madabhushi has authored over 400 peer-reviewed publications and over 100 patents issued or pending. He is a, Fellow of the American Institute of Medical and Biological Engineering (AIMBE), and the Institute for Electrical and Electronic Engineers (IEEE) and the National Academy of Inventors (NAI). His work on "Smart Imaging Computers for Identifying lung cancer patients who need chemotherapy" was called out by Prevention Magazine as one of the top 10 medical breakthroughs of 2018. In 2019, Nature Magazine hailed him as one of 5 scientists developing "offbeat and innovative approaches for cancer research". Dr. Madabhushi was named to The Pathologist’s Power List in 2019, 2020 and 2021.

Talk
AI for Radiology and Pathology for Precision Medicine
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 2022 Silicon Valley


Track Chairs:
Sharmila Majumdar, UCSF
Matthew Lungren, Stanford

The use of Artificial Intelligence (AI) in diagnostic medical imaging is undergoing extensive evaluation. AI has shown impressive accuracy and sensitivity in the identification of imaging abnormalities and promises to enhance tissue-based detection and characterization. This track will explore technical advancements in clinical machine learning and use cases in radiology.

Sessions:

  • Regulatory Considerations for AI & Machine Learning in Medical Imaging
    - Bakul Patel, FDA
    - Sean Khozin, CancerLinQ
  • AI Roadmap: Opportunity and Challenges
    Session Chair: Sean Khozin, CancerLinQ
    - Brandon Allgood, Valo Health
    - Patrick Loerch, Gilead
    - Daniel J. Arbess, Xerion Investments LLC
    - Matthew Lungren, Stanford
  • Technical Advancements in Clinical Machine Learning - What's New in 2021
    Session Chair: Sharmila Majumdar, UCSF
    - Matthew Lungren , Stanford
    - Jayashree Kalpathy-Cramer, Harvard
    - John Mongan, UCSF
  • Image Applications for Clinical Diagnosis
    Session Chair: Lawrence Schwartz, Columbia University Medical Center
    - Thorsten Fleiter, U. of Maryland
    - Imon Banerjee, Mayo Clinic
    - Anant Madabhushi, Case Western Reserve University
    - Pratik Mukherjee, UCSF
    - Esther L. Yuh, UCSF
  • Session TBA
    - Christopher Coley, Epredia
  • Evaluation of AI Software for Radiological Applications
    Session Chair: Kyung Hyun Sung, UCLA
    - Akshay Chaudhari, Stanford
    - Mona Flores, NVIDIA
  • PMWC 2022 Showcase (NCI)
    - Eric Horler, AIQ Solutions