Hugging Face Trending Papers
One-Slide Calibration of Pathology Foundation Models
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arXiv Computer Vision
Oct 8
One-Slide Calibration of Pathology Foundation Models
The paper introduces SlideRuler, a method that uses internal controls within a pathology slide to estimate and correct scanner‑induced shifts in tissue representation. By learning a transfer map from paired rescans, SlideRuler can calibrate a frozen foundation model from a single scan, reducing embedding distance by 16.3‑38.5% across two encoders and five SCORPION scanners. A source‑anchored variant further reduces source‑feature displacement while preserving most alignment gains, demonstrating consistent calibration across imaging systems.
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.
arXiv Computer Vision
Sep 22
Patch-to-Global: Random Patch Diffusion for Globally Consistent Megapixel Artifact Inpainting in Whole Slide Images
arXiv:2609.24116v1 Announce Type: new Abstract: Although deep learning has advanced Whole Slide Image (WSI) Analysis, tissue artifacts like bubbles and folds often cause silent failures by concealing...
arXiv Computer Vision
Sep 30
What Makes High-Magnification Knowledge Transferable? A Study of Cross-Resolution Distillation in Whole-Slide Imaging
arXiv:2609.36407v1 Announce Type: new Abstract: Cross-resolution knowledge distillation aims to improve low-magnification whole- slide analysis by transferring high-magnification representations, yet...
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.
arXiv Computer Vision
Sep 2
SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling
arXiv:2609.00396v1 Announce Type: new Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...