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BEGIN:VEVENT
DTSTAMP:20260429T132235Z
DTSTART;VALUE=DATE-TIME:20250623T140000
DTEND;VALUE=DATE-TIME:20250623T150000
SUMMARY:TIA Centre Seminar Series: Edwin D. de Jong (Aignostics)
TZID:Europe/London
UID:20250623-8ac672c4967a59ee01968621b1962b25@warwick.ac.uk
CREATED:20250430T095932Z
DESCRIPTION:Title: Quantifying Pathology Foundation Model Robustness agai
 nst Medical Center Variation: the Robustness Index Abstract: Pathology F
 oundation Models (FMs) hold great promise for healthcare. Their clinical
  application is challenged by well-documented variations between medical
  centers\, which can potentially lead to biased downstream models. To en
 able the use of pathology FMs in clinical practice\, it is essential to 
 ensure they are robust to such variation. This requires the ability to m
 easure and quantify robustness. We measure how strongly current patholog
 y FMs encode biological features like tissue and cancer type\, and how s
 trongly they encode confounding medical center signatures introduced by 
 staining procedure and other differences. The relation between these two
  factors defines the Robustness Index. This novel robustness metric quan
 tifies to what degree biological features dominate confounding features.
  We evaluate the robustness of current pathology FMs. We find that all F
 Ms encode medical centers to some degree\, and that medical center varia
 tion can dominate the organization of the embedding space\, depending on
  the application domain. The influence of this confounding information o
 n the embedding space varies\, and significant differences in the robust
 ness index are observed. The robustness index provides a critical new be
 nchmark for evaluating and improving pathology FMs\, accelerating progre
 ss towards their safe and reliable clinical adoption. Bio: Edwin de Jong
  has been fascinated by how humans and machines can produce intelligent 
 behavior for over three decades. He studied at Delft University of Techn
 ology and received his PhD from the VUB AI Lab in 2000. As a postdoctora
 l researcher at Prof. Jordan Pollack’s DEMO Lab at Brandeis University\,
  he started a research line in representation learning and open-ended se
 lf-improvement which he continued in The Netherlands at Utrecht Universi
 ty. In 2004/2005\, he co-founded Adapticon\, among the first companies w
 orldwide to apply LSTM in industry. After other roles in the tech indust
 ry\, as of 2018 Edwin's focus is on helping AI transform healthcare. At 
 ScreenPoint Medical\, he contributed to the Transpara algorithm which wa
 s found to substantially reduce radiologist workload and identify 29% mo
 re cancer cases in the MASAI randomized controlled trial\; a milestone r
 ecognized as a Notable Advance by Nature Medicine. His current focus is 
 on developing robust pathology and multimodal foundation models as Princ
 ipal Machine Learning Scientist at Aignostics\, a leading European scale
 up in AI-powered pathology\, where he works to advance computational dia
 gnostics for drug discovery and precision medicine. Paper Link: [2501.18
 055] Current Pathology Foundation Models are unrobust to Medical Center 
 Differences How to attend: Either turn up to the event on the day\, or i
 f you want to attend online then please contact Adam Shephard (adam.shep
 hard@warwick.ac.uk) for more details.
LOCATION:FAB 2.48
URL:/fac/cross_fac/tia/seminars/seminars-24-25/#edwi
 n_de_jong
ATTACH:/fac/cross_fac/tia/seminars/seminars-24-25/#e
 dwin_de_jong
CATEGORIES:Applied Computing
LAST-MODIFIED:20250430T095932Z
ORGANIZER;CN=Adam Shephard:
END:VEVENT
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