arXiv:2605. 24253v2 Announce Type: replace-cross Abstract: Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumour regions and reflecting intrinsic morphological heterogeneity.
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:2608.22066v1 Announce Type: cross Abstract: Attention-based multiple instance learning (ABMIL) using pathology foundation model embeddings is effective for slide-level tasks, but exhaustive inf...
The paper introduces Φ-Omni, a self‑supervised learning framework for computational pathology that disentangles synergistic information across histology, genomics, and clinical reports using Partial Information Decomposition. By employing a Synergistic Information Bottleneck and a ΦID objective, the method suppresses redundant signals while maximizing irreducible cross‑modal synergy, leading to improved few‑shot performance on breast and lung whole‑slide image datasets. The authors demonstrate that Φ-Omni outperforms both supervised and other SSL baselines on eight external tasks.
arXiv:2606. 07590v1 Announce Type: cross Abstract: Pathology foundation models are pretrained on large streams of WSI-derived patches, while supervision during data construction is often slide-level, sparse, or heterogeneous.
MultiAttenGastro is a plug‑and‑play attention framework that adds parallel 1‑D channel, 2‑D spatial, and 3‑D contextual heads to existing CNN and transformer backbones for gastrointestinal endoscopy classification. Across eight backbones and five public GI datasets, the framework improves performance on large‑gap datasets such as Kvasir‑Capsule but shows no benefit on small‑gap benchmarks like Kvasir‑v2, with mixed results elsewhere. Analysis using Centered Kernel Alignment indicates that the gains are linked to representational redundancy: low inter‑head redundancy under large domain gaps yields consistent improvements, while high redundancy under small gaps leads to losses.
Deep learning image classifiers achieve strong predictive performance yet remain opaque in how decisions are formed. A model may predict correctly while relying on irrelevant cues, shortcut associations, peripheral structures, or device level artifacts instead of task relevant regions.
arXiv:2607. 06889v1 Announce Type: cross Abstract: Deep learning image classifiers achieve strong predictive performance yet remain opaque in how decisions are formed.
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...
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.
Whole-slide multiple-instance learning (MIL) observes only the patches admitted by its selector. Deployment can alter this selector through compute limits, tissue masking, or regional workflows, even...
arXiv:2609.10001v1 Announce Type: new Abstract: Few-shot medical image segmentation (FSMIS) seeks to delineate unseen structures from a small support set, but its standard formulation fixes task-defi...