Speaker: Prateek Prasanna, PhD, Department of Biomedical Informatics, Stony Brook University, NY
Date: Tuesday, August 29th, 2023
Time: 10:00AM Central Time
Location: Webex (https://uwmadison.webex.com/uwmadison/j.php?MTID=m285bd9231ee167a6024155196d3f194f)
Title: “Medical Vision With Imperfect Data”
Abstract: The effectiveness of diagnostic and prognostic tools is frequently determined by the accurate and timely analysis of imaging presentations. The efficacy of these data-centric techniques greatly hinges on an extensive corpus of high volume and properly labeled training data. In practice, especially in biomedical contexts, the gathering of labeled data can be cost-prohibitive and time-consuming. The dependency on diverse, high-quality datasets substantially limits model applicability in complex real-world scenes where data is usually imperfect. In this presentation, we will discuss our research efforts in developing computational imaging biomarkers and frameworks for precision medicine in scenarios involving imperfect data. We will cover a spectrum of computational techniques grounded in both biological and domain-specific insights that facilitate the early detection and evaluation of treatment responses across various diseases. These features and methods, inspired by the expertise of clinicians provide a comprehensive understanding of the systemic nature of diseases, thereby establishing a foundation for enhancing clinical decision-making paradigms in radiology and pathology.
Bio: Prateek Prasanna is an Assistant Professor in the Department of Biomedical Informatics at Stony Brook University and directs the Imaging Informatics for Precision Medicine (IMAGINE) Lab. He received his PhD in Biomedical Engineering from Case Western Reserve University, Ohio, USA. Prior to that, he obtained his master’s degree in Electrical and Computer Engineering from Rutgers University and bachelor’s degree in Electrical and Electronics Engineering from National Institute of Technology, Calicut, India. Dr. Prasanna’s research focuses on building clinically translatable machine learning tools that leverage multiple data streams of imaging, pathology, and genomics to derive actionable insights for enabling better treatment decisions. His research involving development of companion diagnostic tools for thoracic, neuro, and breast imaging applications has been published in venues such as MICCAI, CVPR, ECCV, ICLR, Radiology, Medical Image Analysis, etc, and has won several innovation awards. One of the core focuses of his lab is to integrate machine generated inferences with expert clinical reads to make clinical workflows more efficient and effective. Lately, his team has been actively working on the advancement of interpretable machine learning techniques to facilitate the discovery of computational biomarkers, particularly in situations where data is limited or missing.
Publications: https://scholar.google.com/citations?user=uyA1Q18AAAAJ&hl=en&oi=ao