An interpretable cell-centric representation of pancreatic ductal adenocarcinoma matches multiple-instance learning for clinical outcomes and leads to novel pathologist-driven histological biomarkers
Current state of the art computational pathology foundation models attain satisfying accuracy most general pathology tasks including detection of pancreatic ductal adenocarcinoma (PDAC) but fail to provide sufficient explainability, operating as "black boxes". Some interpretability effort has been made, with models pro...