arXiv Machine Learning
InfoDPP-PAC: Principled Patch Selection for Whole Slide Image Analysis
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arXiv Computer Vision
4d ago
With foundation-model features, aggregator complexity does not improve cross-hospital discrimination in gastric whole-slide image classification
The study evaluated whole‑slide image classifiers for gastric cancer across five Korean hospitals, using foundation‑model features and leave‑one‑hospital‑out validation. A hierarchical patch‑region‑slide graph aggregator (CHIME‑MIL) with site alignment and two‑scale fusion achieved a mean AUC of 0.837, outperforming six conventional attention and transformer aggregators that lacked these steps. However, when matched with the same alignment and fusion, CHIME‑MIL did not surpass simple attention‑based pooling, and global max pooling performed best (AUC 0.855). The alignment step, which subtracts each hospital’s mean feature vector without labels, enabled onboarding of a new hospital from only ten unlabeled slides but yielded only a modest 0.002 AUC improvement, and the model’s accuracy gains were inconsistent. "whyItMatters":"The findings show that adding complex aggregation methods does not necessarily improve cross‑hospital discrimination, highlighting the importance of simpler, label‑free onboarding and calibration checks for deployable pathology systems."
arXiv Computer Vision
Oct 8
Masked Feature Encoding for Large-Scale Whole Slide Image Representation
arXiv:2610.10225v1 Announce Type: new Abstract: Whole slide image (WSI) analysis in computational pathology follows a multiple instance learning (MIL) pipeline where patch embeddings are extracted in...
arXiv AI
Jun 2
CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval
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.
arXiv Computer Vision
Aug 25
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 AI
Aug 25
ADMIL: Attention-Distilled Multiple Instance Learning for Selective Foundation Model Inference in Pathology
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...
arXiv Computer Vision
Sep 3
Synergistic Information Disentanglement for Omni-modal Slide Representation Learning in Computational Pathology
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.