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Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification

Paper recorded by Signals 4 on 2026-09-23 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-23 on arXiv · recorded by Signals 4 on 2026-09-24

Category: cs.AI · 人工智能 · first seen 2026-09-24

Abstract

Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PFMs provide rich, discriminative representations for whole slide image (WSI) analysis, enabling tasks such as slide-level classification under multiple instance learning (MIL). However, these representations may also encode non-biological signals asso

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Cite this page: Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification: the #18 most recent of 380 cs.AI papers we have recorded (as of 2026-09-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/do-center-biases-propagate-robustness-of-pathology-foundation-models-in-whole-sl.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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