arXiv AI
Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocols
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arXiv Computer Vision
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LanGuSTE: Language-Guided Coarse-to-Fine Patch Selection for Efficient Whole Slide Image Analysis
LanGuSTE is a patch‑selection framework for whole slide image analysis that uses vision‑language models and large language model knowledge. It introduces Cross‑Scale Visual Prompt Tuning to align low‑resolution and high‑resolution patches, and a coarse‑to‑fine selection module that encodes only informative high‑resolution patches. Experiments show LanGuSTE cuts overall processing time to about one‑third of the baseline while matching or surpassing diagnostic performance of exhaustive and state‑of‑the‑art methods.
arXiv Computer Vision
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SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling
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arXiv Machine Learning
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Morphology signal in whole slide image foundation models can automatically triage slides
arXiv:2609.01987v1 Announce Type: cross Abstract: Patient exams in the cancer diagnosis and staging process typically generate several whole slide images (WSIs). One of the initial steps in training...
arXiv Computer Vision
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Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction
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...
arXiv AI
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EviPathBench: Benchmarking Evidence Acquisition and Reasoning in Vision-Language Models for Whole-Slide Pathology
arXiv:2607.19261v4 Announce Type: replace-cross Abstract: Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating...
arXiv AI
2d ago
SlideBank: A Persistent Hierarchical Evidence Bank for Consistent Whole-Slide Reasoning
SlideBank is a training‑free framework that turns each whole‑slide image into a persistent, concept‑indexed evidence bank. It performs coarse‑to‑fine exploration to locate informative regions and multi‑scale views, converts them into explicit morphological observations, and anchors pathology signals to the supporting patches and slide coordinates. During inference, questions are routed to relevant signals and evidence scales, and a confidence‑based cross‑level consensus integrates global, regional, and patch evidence, achieving high accuracy on WSI‑VQA and SlideBench‑BCNB while enabling consistent re‑phrasing and reduced inference cost.