CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval
arXiv:2605.24253v3 Announce Type: replace-cross Abstract: Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumor regions and reflecting intrinsic morphological heterogeneity. However, most existing approaches rely on a single pathologist-selected slide, thereby discarding potentially informative evidence distributed across the remaining WSIs. To date, no autonomous framework has been proposed for comprehensive multi-WSI case processing. Here, we present an unsupervised framework for case-level analysis that integrates inf
The proliferation of digital pathology archives with multiple whole-slide images per case necessitates advanced computational methods to utilize this rich data effectively, which current approaches largely ignore.
This development allows for a more comprehensive and automated analysis of digital pathology data, potentially leading to more accurate diagnoses and improved research insights in medical AI.
Pathology case representation and retrieval can now move beyond single-slide reliance to integrate information from multiple WSIs, enabling more robust diagnostic and research tools.
- · AI healthcare startups
- · Digital pathology providers
- · Medical researchers
- · Patients
- · Traditional pathology solution providers
- · Manual analysis workflows
Improved accuracy and efficiency in cancer diagnosis and prognostic assessment through better utilization of digital pathology data.
Acceleration of drug discovery and personalized medicine by providing richer, AI-powered insights from pathology archives.
Potential for new AI-driven diagnostic services to emerge, further integrating AI into clinical decision-making processes.
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Read at arXiv cs.AI