Title: THREADS: A Molecular-driven Foundation Model for Oncologic Pathology

Abstract: Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation models are still limited in their ability to encode the entire gigapixel whole-slide images without additional training and often lack complementary multimodal data. In this talk, I will introduce THREADS, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. THREADS was pretrained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections, paired with corresponding genomic and transcriptomic profiles—the largest such paired dataset to be used for foundation model development to date. This unique training paradigm enables THREADS to capture the tissue’s underlying molecular composition, yielding powerful 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 THREADS outperformed all baselines while demonstrating remarkable generalizability and label efficiency. It is particularly well-suited for predicting rare events, further emphasizing its clinical utility.

Bio: Anurag Vaidya is a fourth-year PhD student in the Health Sciences Technology program 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 diagnosis, prognosis, and biomarker discovery.

Paper Link: [2501.16652] Molecular-driven Foundation Model for Oncologic PathologyLink opens in a new windowLink opens in a new windowLink opens in a new 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.shephard@warwick.ac.uk) for more details.