Beyond the Failures: Rethinking Foundation Models in Pathology

📝 Original Info

• Title: Beyond the Failures: Rethinking Foundation Models in Pathology
• ArXiv ID: 2510.23807
• Date: 2025-10-27
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (필요 시 원문 또는 DOI를 확인해 주세요.) **

📝 Abstract

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.

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