You can make medical imaging foundation models 35% faster and 25% smaller by intelligently pruning redundant components, without sacrificing accuracy—and the pruned model is actually more trustworthy at detecting when it might fail.
TAP-Path compresses a large pathology AI model (Virchow2) by removing unnecessary transformer blocks and image patches while keeping it accurate. The method reduces model size by 25% and computation by 35%, maintaining strong performance on histopathology image classification while improving reliability metrics like calibration and failure detection.