Integrative Cancer Pathology Evaluation via Multimodal Artificial Intelligence
Attend the December NCI Imaging Community Webinar to learn how artificial intelligence (AI) can advance cancer pathology evaluation.
Harvard Medical School’s Dr. Kun-Hsing Yu will discuss his work developing a fully automated AI algorithm that extracts thousands of features from whole-slide histopathology images. Histopathology involves examining tissue samples and cells under a microscope, and such a study has applications for cancer diagnosis.
The Cancer Imaging Program is part of NCI’s Division of Cancer Treatment & Diagnosis. The program hosts this monthly webinar series for scientists and clinicians interested in advancing cancer imaging. If you want to learn about upcoming opportunities for engagement, interdisciplinary collaboration, and current research showcases, you can explore upcoming events in this series on the NCI Imaging Community Webinar Series webpage.
Dr. Yu earned his M.D. from the National Taiwan University and his Ph.D. in biomedical informatics, with a minor in computer science, from Stanford University. He joined Harvard Medical School in 2020 as an assistant professor in the Department of Biomedical Informatics. He developed the first fully automated artificial intelligence (AI) algorithm to extract thousands of features from whole-slide histopathology images and successfully identified previously unknown cellular morphologies associated with patient prognosis. Dr. Yu’s research focuses on the integration of quantitative histopathology image patterns with multi-omics profiles to advance cancer research and clinical practice. His research interests include quantitative pathology, machine learning, and translational bioinformatics.
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