Zhangsheng Yu, PhD, Professor in the School of Life Sciences and Biotechnology at the Shanghai Jiao Tong University will be giving a presentation on  “A Foundation Model for Generalizable Cancer Diagnosis and Survival Prediction from Histopathological Images.” This presentation is entirely virtual, and will occur on May 6, 2025, 12:00pm - 1:00pm via Zoom. Registration is required, and upon registration, attendees will received a confirmation email with information about joining the meeting.

ABSTRACT: Extensive histopathological data and the robustness of self-supervised models in small-scale data demonstrate promising prospects for developing foundation pathology models. In this work, we propose the BEPH (BEiT-based model Pre-training on Histopathological images), a general method that leverages self-supervised learning to learn meaningful representations from 11 million unlabeled histopathological images. These representations are then efficiently adapted to various tasks, including patch-level cancer recognition, WSI-level cancer classification, and survival prediction for multiple cancer subtypes. Experimental results demonstrate that our model consistently outperforms several comparative models, even with limited training data reduced to 50%. Especially when the downstream structure is the same, the model can improve ResNet and DINO by up to a maximum increase of 8.8% and 7.2% (WSI level classification), and 6.44% and 3.28% on average (survival prediction), respectively. Therefore, BEPH offers a universal solution to enhance model performance, reduce the burden of expert annotations, and enable widespread clinical applications of artificial intelligence. The code and models are here: github.com/Zhcyoung/BEPH. And online fine-tuning of WSI classification tasks is available for use here: yulab-sjtu.natapp1.cc/.