Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue

2026-09-02

AI Summary

研究解决了脑活检中因采样误差导致疾病未被诊断的问题，通过比较四种病理基础模型在多实例学习框架下的性能，发现即使在非病变组织中也能识别出疾病信号。

Abstract

Sampling error yields exclusively reactive, non-lesional brain parenchyma in a significant proportion of intracranial biopsies, leaving the underlying disease undiagnosed. We benchmark four pathology foundation models (UNI2-h, Virchow2, Prov-GigaPath, H-optimus-0) as frozen patch encoders within a shared attention-based multiple-instance learning framework using 245 whole-slide images from 186 patients with confirmed downstream diagnoses. We first show that coarse disease-category prediction can be reproduced largely from slide size alone. After restricting classification to three finer diagnostic distinctions within common tissue categories, this confound no longer explains performance, yet disease labels remain predictable above chance under permutation testing (p $\le 10^{-4}$ throughout). Surprisingly, performance is statistically indistinguishable across all foundation-model encoders, suggesting that recovering these weak morphological signatures is not limited by current patch representations. Signed instance-contribution maps and expert review further test whether predictive evidence localizes to reactive parenchyma rather than sampling-induced bias like blood introduced during tissue sampling. These results position acquisition-shortcut auditing via a provenance-only baseline as a necessary control in computational-pathology benchmarks, and show, once that confound is removed, that weakly supervised models still recover disease signal from tissue conventionally regarded as non-diagnostic.

Problem

• Sampling error
• Reactive tissue
• Disease recognition
• Intracranial biopsies

Innovation

• pathology foundation models
• attention-based multiple-instance learning
• reactive parenchyma
• acquisition-shortcut auditing