Beyond the Failures: Rethinking Foundation Models in Pathology

Fuente: arXiv
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Main Author: Tizhoosh, Hamid R.
Format: Preprint
Published: 2025
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author Tizhoosh, Hamid R.
author_facet Tizhoosh, Hamid R.
contents Despite their successes in vision and language, foundation models have stumbled in pathology, revealing low accuracy, instability, and heavy computational demands. These shortcomings stem not from tuning problems but from deeper conceptual mismatches: dense embeddings cannot represent the combinatorial richness of tissue, and current architectures inherit flaws in self-supervision, patch design, and noise-fragile pretraining. Biological complexity and limited domain innovation further widen the gap. The evidence is clear-pathology requires models explicitly designed for biological images rather than adaptations of large-scale natural-image methods whose assumptions do not hold for tissue.
format Preprint
id arxiv_https___arxiv_org_abs_2510_23807
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Beyond the Failures: Rethinking Foundation Models in Pathology
Tizhoosh, Hamid R.
Artificial Intelligence
Computer Vision and Pattern Recognition
Despite their successes in vision and language, foundation models have stumbled in pathology, revealing low accuracy, instability, and heavy computational demands. These shortcomings stem not from tuning problems but from deeper conceptual mismatches: dense embeddings cannot represent the combinatorial richness of tissue, and current architectures inherit flaws in self-supervision, patch design, and noise-fragile pretraining. Biological complexity and limited domain innovation further widen the gap. The evidence is clear-pathology requires models explicitly designed for biological images rather than adaptations of large-scale natural-image methods whose assumptions do not hold for tissue.
title Beyond the Failures: Rethinking Foundation Models in Pathology
topic Artificial Intelligence
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2510.23807