Pathology Foundation Model

Pathology foundation models are large-scale AI models trained on vast datasets of digitized pathology images to extract clinically relevant information from whole slide images (WSIs). Current research focuses on improving model generalization across diverse tissue types, stains, and scanners, often employing self-supervised learning and techniques like stain normalization to mitigate WSI-specific feature collapse. These models show promise for improving diagnostic accuracy, biomarker prediction, and prognostic modeling, ultimately aiding pathologists and enhancing patient care. Efforts are underway to create more robust and interpretable models, and to establish standardized benchmarks for evaluating their performance across a wide range of clinical tasks.