Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue
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, ...
| Authors | Jan Schnorrenberg, Jan Ernsting, Enrico Küllenberg et al. |
|---|---|
| Primary category | eess.IV |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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