面向全切片图像的簇级稀疏多示例学习
Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images
AI总结：
针对多示例学习（MIL）中实例冗余和缺乏丢弃非信息性实例机制的问题，提出簇级稀疏MIL（csMIL）框架，通过整合全局-局部聚类、簇内注意力和簇级稀疏正则化，有效提升鲁棒性与可解释性，并在两个组织病理学基准上达到最优性能。
摘要：
多示例学习（MIL）对于分析复杂、弱标记的数据集至关重要，例如计算病理学中的全切片图像（WSIs），其中包由无序的实例集合组成，且具有稀疏的诊断相关性。传统的MIL方法，包括早期的统计方法和近期基于注意力的框架，难以处理实例冗余，并且缺乏丢弃非信息性实例的显式机制，限制了其鲁棒性和可解释性。我们提出了簇级稀疏MIL（csMIL），这是一个整合全局-局部实例聚类、簇内注意力和簇级稀疏诱导以应对这些挑战的新颖框架。我们的csMIL首先跨所有包执行全局聚类以建立$K$个簇中心，随后在每个包内进行局部聚类以分配簇标签。在每个簇内计算注意力分数，并对簇权重应用稀疏正则化，从而能够选择性地保留具有诊断相关性的簇，同时丢弃不相关的簇。这种方法增强了对噪声实例的鲁棒性，通过识别关键区域提高了可解释性，并降低了计算复杂度。理论分析表明，csMIL需要$O(s log K)$个包来恢复$s$个相关簇，这与压缩感知原理相一致。在实验中，csMIL在两个公开的组织病理学基准（CAMELYON16，TCGA-NSCLC）上实现了最先进的性能。
英文摘要：
Multi-Instance Learning (MIL) is pivotal for analyzing complex, weakly labeled datasets, such as whole-slide images (WSIs) in computational pathology, where bags comprise unordered collections of instances with sparse diagnostic relevance. Traditional MIL approaches, including early statistical methods and recent attention-based frameworks, struggle with instance redundancy and lack explicit mechanisms for discarding non-informative instances, limiting their robustness and interpretability. We propose Cluster-level Sparse MIL (csMIL), a novel framework that integrates global-local instance clustering, within-cluster attention, and cluster-level sparsity induction to address these challenges. Our csMIL first performs global clustering across all bags to establish $K$ cluster centers, followed by local clustering within each bag to assign cluster labels. Attention scores are computed within each cluster, and sparse regularization is applied to cluster weights, enabling the selective retention of diagnostically relevant clusters while discarding irrelevant ones. This approach enhances robustness to noisy instances, improves interpretability by identifying critical regions, and reduces computational complexity. Theoretical analysis demonstrates that csMIL requires $O(s log K)$ bags to recover $s$ relevant clusters, aligning with compressed sensing principles. Empirically, csMIL achieves state-of-the-art performance on two public histopathology benchmarks (CAMELYON16, TCGA-NSCLC).
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