Whole-Slide Image Analysis

Whole-slide image analysis studies how to extract clinically useful information from gigapixel tissue scans, where relevant cells and tissue patterns may be sparse and spread across multiple scales. Common approaches divide slides into patches and use multiple-instance learning with slide-level labels, while newer methods add spatial graphs, multiscale models, pathology foundation models, or vision-language systems. These methods support tasks such as cancer classification, grading, lesion and cell localization, survival prediction, molecular profiling, and report generation. A central challenge is balancing accuracy and clinically grounded evidence with computational cost and reliable generalization across hospitals, scanners, and patient cohorts.