VI Seminar #97: Foundation Models for Pathology and the need for Robust Representations

Presented by Vilde Schulerud Bøe, PhD Candidate at SFI Visual Intelligence's hub at the University of Oslo

Abstract

Deep learning has transformed computational pathology, enabling highly accurate models for tasks such as survival analysis and tumor detection. However, despite impressive progress, these models often struggle to maintain performance across domain shifts arising from variations in staining, scanners, institutions, and patient populations. This highlights the challenge of robustness and generalization.

In this talk, I will discuss emerging efforts to build pathology models that are not only accurate, but also reliable across diverse real-world settings. Focusing on foundation models and multiple-instance learning (MIL), we will look at how sources of dataset-specific biases become embedded in learned representations and how they affect downstream performance. I will present current approaches for improving robustness, including our work on encouraging consistent representations across scanner variations during MIL training. I aim to highlight the progress that has been made and the challenges that remain in developing pathology AI systems that generalize beyond the conditions in which they were trained.

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