The paper introduces CytoCRF, a conditional random field framework tailored for cytology images. It adapts pairwise terms to focus on chromatin and cytology-specific staining and enriches neighborhood information by combining multiple backbone models. Across ten cytology datasets, CytoCRF surpasses existing CRF methods at all annotation budgets, achieving up to +13.6 percentage points over the best baseline and +33.7 over zero‑shot performance with only 50 annotations.
arXiv:2609.31314v1 Announce Type: new Abstract: Cytopathology detection requires open-vocabulary recognition because cellular categories are fine-grained, long-tailed, and continuously evolving acros...
arXiv:2609.36429v1 Announce Type: new Abstract: Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods...
arXiv:2608. 10657v1 Announce Type: cross Abstract: Leukemia cell image classification is challenged by real-world domain shifts from acquisition, staining, illumination, and site protocols, causing single-dataset models to generalize poorly in real clinical scenarios.
arXiv:2602.06674v2 Announce Type: replace-cross Abstract: High-quality annotated datasets are crucial for advancing machine learning in medical image analysis. However, a critical gap exists: most da...
arXiv:2511. 05150v2 Announce Type: replace-cross Abstract: Molecular biomarker testing in pathology is often costly and tissue-consuming, limiting scalable clinical deployment.
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:2608.30420v1 Announce Type: cross Abstract: Automating the analysis of whole-slide images has high clinical value, since characterizing cancers requires examining them in detail. Such analysis...
Recent advances in pathology foundation models have enabled accurate prediction of spatial transcriptomics (ST) from routine H&E images. However, existing explainability methods for vision transformer...
The paper introduces SlideTIM, a transductive few‑shot classification method tailored for whole‑slide images (WSIs). SlideTIM extends the LC‑TIM approach by adding a spatial‑latent regularizer and a class‑distribution prior, ensuring that spatially and semantically similar patches receive consistent predictions and that predicted class proportions are calibrated. Experiments on four histology datasets show that SlideTIM outperforms existing TIM variants, boosting macro‑F1 scores by up to 8.1 percentage points over the best baseline and 19.4 percentage points over zero‑shot predictions at one shot.
arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.
SheafStain introduces a sheaf-theoretic Schr"odinger Bridge framework to improve virtual staining of gigapixel whole-slide images. By treating Vision Foundation Model features as sheaf-like sections, it integrates class and patch tokens to enforce spatial and biological coherence, mitigating patch-boundary artifacts. The method is evaluated on HER2, ER, PR, and Ki‑67 stains, outperforming six prior approaches on stitched 1024 × 1024 outputs.
WILSON is a vision–language foundation model that represents whole‑slide images and multi‑slide patient cases as single multi‑magnification composite images. Trained on about 189,000 Mayo Clinic slides covering 42 organs and 829 diagnostic entities, it outperforms dedicated case‑level models on internal cohorts and matches slide‑level models while using far less compute. Fine‑tuning on triple‑negative breast cancer data improves histologic subtyping and lymphocyte grading, and the model retrieves diagnostic text with high recall and generates captions closer to report references than prior methods.