Histopathology Image Classification

Histopathology image classification studies how to recognize disease and clinically relevant tissue patterns in microscope images, from small patches and cells to whole-slide images (WSIs). Common approaches include convolutional and vision-transformer models, pathology foundation models, and multiple-instance learning (MIL), which uses slide-level labels to combine evidence from many patches; newer methods also model spatial structure, multiple magnifications, and clinical or molecular data. These systems are evaluated on tasks such as tumor detection, cancer subtyping, grading, and biomarker prediction, using public benchmarks and increasingly external, multicenter, or prospective clinical settings. A central challenge is maintaining reliable performance across sites, stains, scanners, and patient groups while ensuring models use meaningful diagnostic evidence and provide calibrated, interpretable predictions at feasible computational cost.