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arXiv AI By Mehedi Hasan, Ashfak Yeafi, Md Khairul Islam

TAP-Path: Task-Adaptive Structural and Token Pruning for Efficient and Trustworthy Pathology Foundation Models

Read the original on arXiv AI →

TAP-Path is a task‑adaptive compression framework that restructures a pretrained Virchow2 encoder for histopathology. It selectively removes transformer blocks, prunes patch tokens, and adds a lightweight gated task head, reducing parameters by 24.96% and FLOPs by 35.20% while maintaining high accuracy on a 32‑class benchmark. The method achieves competitive test metrics and improved rare‑class performance, with strong external validation on CPTAC samples.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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