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arXiv Computer Vision By Jan Schnorrenberg, Jan Ernsting, Enrico K\"ullenberg, Tim Hahn, Benjamin Risse, Christian Thomas

Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue

Read the original on arXiv Computer Vision →

The study evaluates whether disease can be identified from reactive, non‑lesional brain tissue in intracranial biopsies. Using four foundation‑model encoders within an attention‑based multiple‑instance learning framework on 245 whole‑slide images, the authors find that disease labels remain predictive even after controlling for slide size and sampling bias, and that performance is similar across all encoders. Signed instance‑contribution maps and expert review confirm that predictive signals localize to reactive parenchyma rather than artifacts such as blood. "whyItMatters":"The findings demonstrate that weakly supervised models can recover disease signals from tissue traditionally considered non‑diagnostic, highlighting the need for provenance‑only baselines in computational pathology benchmarks."

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