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Research questionHow can pathology foundation models be compressed to reduce inference cost without sacrificing classification accuracy or reliability?Large pathology encoders are expensive to run because of their parameter counts and extensive patch processing. Removing computation can also discard information needed for accurate or well-calibrated classification, especially across diverse tissue classes.
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Latest papersRecent research connected to this question, newest first.TAP-Path: Task-Adaptive Structural and Token Pruning for Efficient and Trustworthy Pathology Foundation ModelsThe evidence concerns a pretrained Virchow2 encoder for a 32-class histopathology benchmark, with reported parameter and FLOP reductions, classification and reliability metrics, and frozen external evaluation on 433 CPTAC samples. The results are limited to these tasks, datasets, and evaluation settings.research paper · Sep 3, 2026
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