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BEGIN:VEVENT
DTSTAMP:20260430T223351Z
DTSTART;VALUE=DATE-TIME:20250217T140000
DTEND;VALUE=DATE-TIME:20250217T150000
SUMMARY:TIA Centre Seminar Series: Lucy Godson (National Pathology Imagin
 g Co-operative\, Leeds)
TZID:Europe/London
UID:20250217-8ac672c5948707dc01948d81e3ea3db0@warwick.ac.uk
CREATED:20250122T102533Z
DESCRIPTION:Title: Predicting melanoma patient outcomes using digital pat
 hology Abstract: Melanoma is the most aggressive form of skin cancer and
  fifth most common cancer in the UK. Identifying novel early-stage progn
 ostic biomarkers and determining effective treatments are two key challe
 nges for helping melanoma patients get better outcomes. Previous studies
  have analysed genetic data from tumours to stratify patients into immun
 e subgroups\, which were associated with differential melanoma specific 
 survival and potential predictive biomarkers. However\, this genetic ana
 lysis is not carried out in current clinical workflows\, whereas haemato
 xylin and eosin (H&E) stained slides are routinely used in patient diagn
 osis. This talk will present our work on how deep learning models can be
  used to classify whole slide images (WSIs)\, into these molecular immun
 e subgroups. I will discuss the application of different multiple instan
 ce learning (MIL) frameworks and examine how image resolution\, feature 
 extraction methods and aggregation strategies can affect model performan
 ce. I will also argue that graph representations can be used to encode s
 patial and contextual information within WSIs to improve immune subtype 
 classifications. Finally\, I will present our work on survival graph neu
 ral networks\, for discovering new patient risk groups based on melanoma
  specific survival. Bio: Lucy currently works as a Digital Pathology AI 
 Scientist at the National Pathology Imaging Cooperative (NPIC). Her work
  focuses on developing advanced AI tools for better understanding melano
 ma patient outcomes. This involves creating image analysis pipelines and
  collaborating closely with pathologists to design tools that can improv
 e melanoma treatment and patient care. Before starting her role at NPIC\
 , Lucy carried out her PhD with the Centre for Doctoral Training (CDT) f
 or Artificial Intelligence in Medical Diagnosis and Care at the Universi
 ty of Leeds. Her research\, titled “Predicting melanoma patient outcomes
  using digital pathology” investigated the use of multiple instance lear
 ning\, graph neural networks and survival analysis techniques to classif
 y whole slide images. 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.shephard@warwick.ac.uk) for more details.
LOCATION:MB 2.23
URL:/fac/cross_fac/tia/seminars/seminars-24-25/#lucy
 _godson
ATTACH:/fac/cross_fac/tia/seminars/seminars-24-25/#l
 ucy_godson
CATEGORIES:Applied Computing
LAST-MODIFIED:20250122T102533Z
ORGANIZER;CN=Adam Shephard:
END:VEVENT
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