Computational Pathology

Computational pathology applies computational methods, primarily deep learning, to analyze digitized histopathology slides, aiming to improve diagnostic accuracy, efficiency, and accessibility. Current research emphasizes developing robust and generalizable models, focusing on architectures like Vision Transformers, Multiple Instance Learning (MIL), and foundation models, often incorporating multimodal data (e.g., pathology reports, gene expression) and addressing challenges like domain generalization and efficient WSI representation. This field holds significant promise for improving cancer diagnosis and prognosis, accelerating research, and potentially reducing the workload on pathologists.