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arXiv Computer Vision

Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue

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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Sep 2

Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue

The study evaluates whether disease can be identified from reactive, non‑lesional brain tissue in intracranial biopsies. Using four foundation‑model encoders as frozen patch encoders within an attention‑based multiple‑instance learning framework, the authors benchmark performance on 245 whole‑slide images from 186 patients. They find that coarse disease‑category prediction can be largely explained by slide size, but finer diagnostic distinctions remain predictive above chance, with no significant difference among the encoders, suggesting weak morphological signals can be recovered even from tissue traditionally considered non‑diagnostic.

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arXiv Machine Learning
Oct 12

The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging

The paper introduces TAP-JEPA, a joint‑embedding predictive architecture trained on over 113,000 CT volumes, which achieves performance comparable to leading encoders in scan‑level classification. It demonstrates a predictability bias: while anatomy is largely predictable from context, pathology is not, leading to significant degradation in pathology detection when predictions are applied. The study audits the model’s predictive pathway, showing that pathology representations shift toward control regions and that removing pathology tokens minimally affects downstream decisions, underscoring that contextual predictability alone is insufficient for clinical relevance.

By Fabian Drexel, Bernhard Kainz, Jakob N. Kather, Daniel Rueckert, Lisa C. Adams, Keno K. Bressem, Cosmin I. Bercea
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arXiv Computer Vision
Sep 30

HERO: Histology Encoder for Robust Representation in Oncology

HERO (Histology Encoder for Robust Representation in Oncology) is a ViT‑G/14 pathology foundation model trained with DINO and iBOT objectives and refined using high‑resolution Gram anchoring on a 500‑million‑tile corpus from about 575,000 clinical whole‑slide images. It demonstrates superior robustness to center, scanner, and stain variation compared to other state‑of‑the‑art foundation models, while maintaining competitive performance on tile‑level classification, segmentation, and gene‑expression prediction. Across 39 slide‑level clinical tasks, HERO ranks first on average and achieves the best average rank across six benchmark frameworks under an equal‑weighted analysis.

By Zhi Li (Caris Life Sciences, Irving, TX, United States), Eghbal Amidi (Caris Life Sciences, Irving, TX, United States), Yating Cheng (Caris Life Sciences, Irving, TX, United States), Tyson Dawson (Caris Life Sciences, Irving, TX, United States), Gorkem Can Ates (Caris Life Sciences, Irving, TX, United States), Shuzhen Kuang (Caris Life Sciences, Irving, TX, United States), Norsang Lama (Caris Life Sciences, Irving, TX, United States), Md Ashequr Rahman (Caris Life Sciences, Irving, TX, United States), Zhiying Lu (Caris Life Sciences, Irving, TX, United States), Elisabeth K. Kong (Caris Life Sciences, Irving, TX, United States), Milan Radovich (Caris Life Sciences, Irving, TX, United States), David Spetzler (Caris Life Sciences, Irving, TX, United States), Matthew Oberley (Caris Life Sciences, Irving, TX, United States), George W. Sledge (Caris Life Sciences, Irving, TX, United States), Ming Chen (Caris Life Sciences, Irving, TX, United States)
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arXiv AI
Jun 12

BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning

arXiv:2604. 27277v3 Announce Type: replace-cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data.

By Yizhou Wu, Shansong Wang, Yuheng Li, Mojtaba Safari, Mingzhe Hu, Chih-Wei Chang, Harini Veeraraghavan, Xiaofeng Yang
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arXiv AI
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Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI

arXiv:2601. 20503v2 Announce Type: replace-cross Abstract: White matter hyperintensities (WMH) and ischaemic stroke lesions (ISL) are key imaging biomarkers of cerebral small vessel disease (SVD) detectable on magnetic resonance imaging (MRI).

By Jesse Phitidis, Alison Q. Smithard, William N. Whiteley, Joanna M. Wardlaw, Miguel O. Bernabeu, Maria Vald\'es Hern\'andez
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