PathoHR is a new pipeline for predicting breast cancer survival from high‑resolution pathological images. It uses a plug‑and‑play Vision Transformer to enhance patch‑wise whole slide image representations, evaluates multiple similarity metrics to optimize feature learning, and shows that smaller, enhanced patches can match or surpass the accuracy of larger raw patches while cutting computational cost. The authors provide experimental evidence that this approach improves both accuracy and efficiency in computational pathology.
arXiv:2510.06113v2 Announce Type: replace Abstract: Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which red...
The paper presents MSMV‑Swin, a hybrid multi‑scale and multi‑view Swin Transformer framework that integrates the Segment Anything Model to isolate breast lobes and reduce background noise. It captures both localized tumor regions and surrounding tissue context, aligning its outputs with radiologists’ interpretation of mammograms. A hybrid fusion structure is added to maintain robustness when one or more mammogram views are missing during testing.
The paper presents a lightweight CNN‑integrated Compact Convolutional Transformer (CCT) designed for multi‑scale feature learning in breast cancer mammography. With only 250,435 parameters, the model achieved 99‑100% accuracy across three datasets using 5‑fold cross‑validation, demonstrating robust generalization. Explainable AI components were added to clarify the classification process, aiming to increase clinical trust in resource‑constrained settings.
MagViT is an interpretable multi‑magnification transformer that classifies breast histopathology images by extracting representations from four BreakHis magnifications (40X, 100X, 200X, 400X) and fusing them with a learnable, scale‑gated mechanism that can mask missing scales. The model selects the most accurate architectural branch at the patient level using five‑fold cross‑validation, achieving high performance on BreakHis (mean image accuracy 0.9191, patient accuracy 0.9643, macro‑F1 0.9042) and demonstrating preliminary cross‑dataset generalization on BUSI and IDC. Grad‑CAM visualizations confirm that the network focuses on diagnostically relevant regions across magnifications.
arXiv:2607. 10406v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has emerged as an effective paradigm for learning transferable representations from large-scale unlabeled whole slide images (WSIs).
The paper introduces a pipeline that uses publicly available whole slide image foundation models (FMs) to automatically triage slides by ranking them based on zero‑shot classification predictions. This approach accurately identifies slides containing the most tumor, achieving top‑2 ranking for patients with up to 43 slides across multiple datasets. The study also proposes a ranked evaluation framework to benchmark FM performance in slide triage.
arXiv:2606. 06983v1 Announce Type: cross Abstract: Computational pathology requires visual representations that transfer across diverse clinical endpoints and remain robust to variation in magnification, staining, scanner type, slide preparation, and input resolution.
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:2410. 00945v2 Announce Type: replace-cross Abstract: Gene-expression profiling is widely used in research and central to many areas of precision oncology, but remains costly and not universally accessible.
MIST is a multimodal survival prediction framework that fuses whole-slide images and genomic profiles by representing genomic features as tokens that query histology context tokens derived from a foundation model. The architecture enriches molecular information with histology context before survival prediction, avoiding late-stage merging of separately encoded modalities. Training incorporates discrete-time survival prediction, genomic feature masking, WSI dropout, and contrastive alignment, and demonstrates improved external C-index across colon, renal, lung, and glioblastoma cohorts compared to standard fusion baselines.
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...
OncoVision is a privileged‑information training framework that learns from mammography images and clinical data during training but performs inference using only mammographic images. It employs an attention‑based encoder‑decoder to jointly segment masses, calcifications, axillary findings, and breast tissue, and predicts ten structured clinical features such as BI‑RADS. Two late‑fusion strategies (Independent and Dependent) integrate imaging, radiomic, and clinical information to improve diagnostic precision, and a retrospective multi‑reader study showed higher diagnostic confidence, reduced reading time, and segmentation accuracy comparable to or better than radiologists.