arXiv Machine Learning By Ruman Wang, Hangting Ye

ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs

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arXiv:2607. 28538v1 Announce Type: cross Abstract: Classifying pathological scars from clinical photographs requires distinguishing keloids from hypertrophic scars despite limited expert-labeled data and substantial acquisition variation across hospitals.

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arXiv Computer Vision
3d ago

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)
arXiv Computer Vision
1d ago

From Image Latent Space to Fuzzy Rules: Interpretable Analysis of Gastrointestinal Foundation Model

arXiv:2610.00414v1 Announce Type: new Abstract: Foundation models pretrained on large-scale datasets demonstrate strong transferability to medical imaging tasks. However, understanding how their late...

By Michael D. Vasilakakis (Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece), Dimitris K. Iakovidis (Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece)