Computational pathology

a field at the intersection of computer science and pathology

leverages digital technology to enhance diagnostic accuracy and efficiency. With the digitization of pathology and the development of artificial intelligence

computational pathology has made significant strides in the automatic analysis of pathology images

including pathological structure segmentation

tumor classification

and prognosis analysis. Driven by large-scale datasets and advanced methods

computational pathology is moving toward building foundation models to reach more general applications. Generative methods provide a new perspective on addressing challenges in computational pathology. However

challenges in data security and model reliability

reproducibility

and clinical application remain. This review outlines the evolution of computational pathology from pathology slide digitization to pathology image analysis

consolidates the development of foundation and generative models in computational pathology

and discusses the key challenges that persist. Finally

we introduce some rising techniques for precision pathology.