SLC-PFM: Self-supervised Learning for Cancer Pathology Foundation Models

The emergence of foundation models has revolutionized artificial intelligence (AI) across various applications \cite{bommasani2021}, with recent advances in computational pathology (for example, UNI \cite{chen2024}, Virchow \cite{vorontsov2024}, GigaPath \cite{xu2024}, etc.) demonstrating potential for improving diagnostic capabilities and patient outcomes. The proposed Competition on Self-supervised Learning for Cancer Pathology Foundation Models (SLC-PFM) provides an unprecedented platform for advancing the development of the next generation of pathology foundation models. Central to this competition is MSK-SLCPFM, the largest pathology dataset to date for purposes of a competition, comprising over 300 million images spanning 39 cancer types that will be provided to participants for pre-training their models with self-supervised learning techniques. The competition follows a two-phase structure: foundation model development followed by evaluation across 23 clinically relevant downstream tasks including biomarker prediction, cancer subtyping, and survival prediction. The competition is designed to be inclusive for a diverse audience of machine learning and AI practitioners, computer scientists, engineers, bioinformaticians, and specialists from related disciplines, regardless of their background in pathology or medical image processing. By eliminating the barrier of domain-specific data curation, the competition enables participants to focus on technical innovation. The key highlights of the proposed competition are -- Comprehensive Pre-training Data: access to the largest pathology data with 300M images enabling foundation model training at scale, Robust Validation Framework: multi-institutional evaluation across diverse clinically relevant pathology tasks, and Focus on Technical Innovation: participants can focus on novel architectures and learning approaches without the burden of data curation.