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BEGIN:VEVENT
DTSTAMP:20260501T103133Z
DTSTART;VALUE=DATE-TIME:20250407T140000
DTEND;VALUE=DATE-TIME:20250407T150000
SUMMARY:TIA Centre Seminar Series: Anurag Vaidya (Harvard Medical School)
 
TZID:Europe/London
UID:20250407-8ac672c595eb93c40195ebbc22be02d0@warwick.ac.uk
CREATED:20250331T103345Z
DESCRIPTION:Title: THREADS: A Molecular-driven Foundation Model for Oncol
 ogic Pathology Abstract: Foundation models are reshaping computational p
 athology by enabling transfer learning\, where models pre-trained on vas
 t datasets can be adapted for downstream diagnostic\, prognostic\, and t
 herapeutic response tasks. Despite these advances\, foundation models ar
 e still limited in their ability to encode the entire gigapixel whole-sl
 ide images without additional training and often lack complementary mult
 imodal data. In this talk\, I will introduce THREADS\, a slide-level fou
 ndation model capable of generating universal representations of whole-s
 lide images of any size. THREADS was pretrained using a multimodal learn
 ing approach on a diverse cohort of 47\,171 hematoxylin and eosin (H&E)-
 stained tissue sections\, paired with corresponding genomic and transcri
 ptomic profiles—the largest such paired dataset to be used for foundatio
 n model development to date. This unique training paradigm enables THREA
 DS to capture the tissue’s underlying molecular composition\, yielding p
 owerful representations applicable to a wide array of downstream tasks. 
 In extensive benchmarking across 54 oncology tasks\, including clinical 
 subtyping\, grading\, mutation prediction\, immunohistochemistry status 
 determination\, treatment response prediction and survival prediction TH
 READS outperformed all baselines while demonstrating remarkable generali
 zability and label efficiency. It is particularly well-suited for predic
 ting rare events\, further emphasizing its clinical utility. Bio: Anurag
  Vaidya is a fourth-year PhD student in the Health Sciences Technology p
 rogram at Harvard and MIT\, where he is supervised by Dr. Faisal Mahmood
  from Harvard Medical School. Anurag completed his undergraduate studies
  in biomedical engineering and computer science at Bucknell University. 
 His doctoral work focuses on developing tools that integrate multimodal 
 clinical data in supervised and unsupervised ways to improve cancer diag
 nosis\, prognosis\, and biomarker discovery. Paper Link: [2501.16652] Mo
 lecular-driven Foundation Model for Oncologic PathologyLink opens in a n
 ew window How to attend: Either turn up to the event on the day\, or if 
 you want to attend online then please contact Adam Shephard (adam.shepha
 rd@warwick.ac.uk) for more details.
LOCATION:MB 2.23
URL:/fac/cross_fac/tia/seminars/seminars-24-25/#anur
 ag_vaidya
ATTACH:/fac/cross_fac/tia/seminars/seminars-24-25/#a
 nurag_vaidya
CATEGORIES:Applied Computing
LAST-MODIFIED:20250331T103345Z
ORGANIZER;CN=Adam Shephard:
END:VEVENT
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