Enabling clinical use of foundation models for computational pathology

ArXi:2602.22347v2 Announce Type: replace-cross Foundation models for computational pathology are expected to facilitate the development of high-performing, generalisable deep learning systems. However, in addition to biologically relevant features, current foundation models also capture pre-analytic and scanner-specific variation that bias the predictions made by downstream task-specific models trained on these features. Here we show that