{"slug": "infodpp-pac-principled-patch-selection-for-whole-slide-image-analysis", "title": "InfoDPP-PAC: Principled Patch Selection for Whole Slide Image Analysis", "summary": "Researchers introduced InfoDPP-PAC, a patch-selection framework for whole-slide image analysis that combines teacher-seeded Gaussian process relevance modeling, determinantal diversity, submodular greedy optimization, and an adaptive stopping rule. On 202 HISTAI gastrointestinal whole-slide images, the adaptive rule uses 83.7% fewer patches on average than a fixed full budget while retaining 97.9% of full-budget composite selection quality, and at a matched budget it achieves the highest mean teacher-derived relevance score among fourteen baselines. The framework is positioned as a controlled quality-diversity-cardinality selection method, not a downstream clinical predictor.", "body_md": "arXiv:2608.23574v1 Announce Type: cross\nAbstract: Each WSI slide contains thousands of candidate tissue patches, while supervision is usually available only at slide level. Existing bag-construction strategies like Uniform extraction and handcrafted heuristics do not control redundancy while attention-based multiple-instance models couple patch importance to a particular downstream classifier, and coreset methods optimise embedding-space coverage without modelling task-relevant patch quality. We introduce InfoDPP-PAC, a principled patch-selection framework that combines teacher-seeded Gaussian process relevance modelling, determinantal log-determinant diversity,submodular greedy optimisation, and a concentration-based adaptive stopping rule. The main theoretical result shows that the log-determinant diversity term used in DPP-style selection is the Gaussian process mutual information between a selected subset and the latent relevance function. We further derive a PAC-style certificate for residual information gain, allowing the number of retained patches to vary by slide rather than being fixed a priori. The empirical study evaluates whether the selected subset is diverse, spatially and morphologically covering, non-redundant, and enriched for the teacher-derived relevance signal. It does not claim end-to-end diagnostic improvement after retraining a downstream MIL model. On 202 HISTAI gastrointestinal whole-slide images, the adaptive rule uses 83.7% fewer patches on average than a fixed full budget while retaining 97.9% of full-budget composite selection quality. At a matched budget, InfoDPP-PAC achieves the highest mean teacher-derived relevance score among fourteen baselines, with diversity and composite scores close to the strongest coreset methods. The results support InfoDPP-PAC as a controlled quality-diversity-cardinality selection framework, rather than as a downstream clinical predictor.", "url": "https://wpnews.pro/news/infodpp-pac-principled-patch-selection-for-whole-slide-image-analysis", "canonical_source": "https://www.machinebrief.com/news/infodpp-pac-principled-patch-selection-for-whole-slide-image-hklj", "published_at": "2026-08-26 04:00:00+00:00", "updated_at": "2026-08-26 04:44:15.033750+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision"], "entities": ["InfoDPP-PAC", "HISTAI"], "alternates": {"html": "https://wpnews.pro/news/infodpp-pac-principled-patch-selection-for-whole-slide-image-analysis", "markdown": "https://wpnews.pro/news/infodpp-pac-principled-patch-selection-for-whole-slide-image-analysis.md", "text": "https://wpnews.pro/news/infodpp-pac-principled-patch-selection-for-whole-slide-image-analysis.txt", "jsonld": "https://wpnews.pro/news/infodpp-pac-principled-patch-selection-for-whole-slide-image-analysis.jsonld"}}