  Foundation models pretrained on large-scale datasets are revolutionizing the
field of computational pathology (CPath). The generalization ability of
foundation models is crucial for the success in various downstream clinical
tasks. However, current foundation models have only been evaluated on a limited
type and number of tasks, leaving their generalization ability and overall
performance unclear. To address this gap, we established a most comprehensive
benchmark to evaluate the performance of off-the-shelf foundation models across
six distinct clinical task types, encompassing a total of 39 specific tasks.
Our findings reveal that existing foundation models excel at certain task types
but struggle to effectively handle the full breadth of clinical tasks. To
improve the generalization of pathology foundation models, we propose a unified
knowledge distillation framework consisting of both expert and self knowledge
distillation, where the former allows the model to learn from the knowledge of
multiple expert models, while the latter leverages self-distillation to enable
image representation learning via local-global alignment. Based on this
framework, a Generalizable Pathology Foundation Model (GPFM) is pretrained on a
large-scale dataset consisting of 190 million images from around 86,000 public
H&E whole slides across 34 major tissue types. Evaluated on the established
benchmark, GPFM achieves an impressive average rank of 1.36, with 29 tasks
ranked 1st, while the the second-best model, UNI, attains an average rank of
2.96, with only 4 tasks ranked 1st. The superior generalization of GPFM
demonstrates its exceptional modeling capabilities across a wide range of
clinical tasks, positioning it as a new cornerstone for feature representation
in CPath.
