发表机构
Motilal Nehru National Institute of Technology Allahabad; Vision Exploration and Data Analytics (VEDAs) Lab（Motilal Nehru 阿拉哈巴德国立技术学院; 视觉探索与数据分析实验室（VEDAs））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对全切片图像分析的补丁选择问题，提出InfoDPP-PAC框架，结合多种方法实现受控的质量-多样性-基数选择，在202张胃肠道WSI上验证其性能优于多数基线。
AI 中文摘要
每张全切片图像（WSI）包含数千个候选组织补丁，而监督信息通常仅以切片级别提供。现有的包构建策略（如均匀提取和手工启发式方法）无法控制冗余，基于注意力的多实例模型将补丁重要性与特定下游分类器耦合，核心集方法则仅优化嵌入空间覆盖范围而未对任务相关的补丁质量进行建模。本文提出InfoDPP-PAC，这是一种原则性补丁选择框架，结合了教师引导的高斯过程相关性建模、行列式点过程（DPP）的对数行列式多样性、子模贪心优化以及基于浓度的自适应停止规则。主要理论结果表明，DPP式选择中使用的对数行列式多样性项是所选子集与潜在相关性函数之间的高斯过程互信息。我们进一步推导了剩余信息增益的PAC风格保证，允许每张切片保留的补丁数量可变，而非预先固定。实证研究评估所选子集是否具有多样性、空间和形态覆盖性、非冗余性，并富含教师衍生的相关性信号。该研究未声称在重新训练下游多实例学习（MIL）模型后可实现端到端诊断改进。在202张HISTAI胃肠道全切片图像上，自适应规则平均使用的补丁数比固定全预算少83.7%，同时保留了97.9%的全预算组合选择质量。在匹配预算下，InfoDPP-PAC在14个基线中实现了最高的平均教师衍生相关性得分，其多样性和组合得分接近最强的核心集方法。结果支持InfoDPP-PAC是一种受控的质量-多样性-基数选择框架，而非下游临床预测器。
英文摘要
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.
