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BEGIN:VEVENT
DTSTAMP:20260501T041910Z
DTSTART;VALUE=DATE-TIME:20250203T140000
DTEND;VALUE=DATE-TIME:20250203T150000
SUMMARY:TIA Centre Seminar Series: Kun-Hsing Yu (Harvard Medical School)
TZID:Europe/London
UID:20250203-8ac672c5948707dc01948d7b097f3d8b@warwick.ac.uk
CREATED:20250122T101008Z
DESCRIPTION:Title: Harnessing AI Foundation Models for Cancer Diagnostics
 : Breakthroughs and Future Prospects Abstract: Artificial intelligence (
 AI) is transforming the landscape of cancer research and clinical diagno
 sis. Recent advances in microscopic image digitization\, multi-modal mac
 hine learning algorithms\, and scalable computing infrastructure have pa
 ved the way for AI-enhanced pathology assessments. In this talk\, I will
  highlight recent breakthroughs in pathology foundation models and their
  effectiveness in analyzing high-resolution digital pathology images. In
  addition\, I will present examples of AI-empowered real-time pathology 
 evaluations during cancer surgery and demonstrate their adaptability to 
 evolving diagnostic classifications. Furthermore\, I will discuss recent
  studies that employed AI to reveal intriguing links between cell morpho
 logy and molecular profiles. Finally\, I will outline ongoing challenges
  in developing robust medical AI systems and identify research direction
 s to address these critical issues. Bio: Kun-Hsing "Kun" Yu\, M.D\, Ph.D
 . is an Assistant Professor in the Department of Biomedical Informatics 
 at Harvard Medical School. He developed the first fully automated artifi
 cial intelligence (AI) algorithm to extract thousands of features from w
 hole-slide histopathology images\, discovered the molecular mechanisms u
 nderpinning the microscopic phenotypes of tumor cells\, and successfully
  identified previously unknown cellular morphologies associated with pat
 ient prognosis. His lab integrates cancer patients' multi-omics (genomic
 s\, epigenomics\, transcriptomics\, and proteomics) profiles with quanti
 tative histopathology patterns to predict their clinical phenotypes. Mor
 e than 30 research laboratories worldwide have independently validated t
 he AI methods developed in the Yu Lab. Paper Link: A pathology foundatio
 n model for cancer diagnosis and prognosis prediction | Nature How to at
 tend: Either turn up to the event on the day\, or if you want to attend 
 online then please contact Adam Shephard (adam.shephard@warwick.ac.uk) f
 or more details.
LOCATION:MB 2.23
URL:/fac/cross_fac/tia/seminars/seminars-24-25/#kun_
 yu
ATTACH:/fac/cross_fac/tia/seminars/seminars-24-25/#k
 un_yu
CATEGORIES:Applied Computing
LAST-MODIFIED:20250122T101008Z
ORGANIZER;CN=Adam Shephard:
END:VEVENT
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