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Spatial biomarker discovery via interpretable semantic learning in histopathology
Liang et al. introduce PathPrism, a framework that converts whole-slide images into interpretable spatial biomarker spectra, establishing a transparent representation of tissue architecture. This representation enables transparent linear modeling of prognosis, molecular alterations, and treatment response with high performance, while transforming histopathology into a platform for interpretable discovery and perturbation-driven exploration.