Abstract: Melanoma is the most aggressive form of skin cancer and fifth most common cancer in the UK. Identifying novel early-stage prognostic biomarkers and determining effective treatments are two key challenges for helping melanoma patients get better outcomes. Previous studies have analysed genetic data from tumours to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, this genetic analysis is not carried out in current clinical workflows, whereas haematoxylin and eosin (H&E) stained slides are routinely used in patient diagnosis. This talk will present our work on how deep learning models can be used to classify whole slide images (WSIs), into these molecular immune subgroups. I will discuss the application of different multiple instance learning (MIL) frameworks and examine how image resolution, feature extraction methods and aggregation strategies can affect model performance. I will also argue that graph representations can be used to encode spatial and contextual information within WSIs to improve immune subtype classifications. Finally, I will present our work on survival graph neural 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 melanoma patient outcomes. This involves creating image analysis pipelines and collaborating closely with pathologists to design tools that can improve melanoma treatment and patient care. Before starting her role at NPIC, Lucy carried out her PhD with the Centre for Doctoral Training (CDT) for Artificial Intelligence in Medical Diagnosis and Care at the University of Leeds. Her research, titled “Predicting melanoma patient outcomes using digital pathology” investigated the use of multiple instance learning, graph neural networks and survival analysis techniques to classify 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.