CGRL：用于全切片图像分类的概念引导剪枝与表示学习
CGRL: Concept-Guided Pruning and Representation Learning for Whole-Slide Image Classification
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中文总结 AI 辅助
针对弱监督全切片图像分类中现有方法问题，提出CGRL框架，通过概念相关剪枝和概念引导对比表示学习，在TCGA - BRCA和TCGA - NSCLC数据集上评估，提升了模型性能并降低计算成本，证明类级语义概念的有效性。
中文摘要 AI 辅助
弱监督全切片图像（WSI）分类在计算病理学中广泛应用，因切片级标签比密集区域注释更易获取。现有多实例学习（MIL）方法常主要基于视觉线索聚合大量补丁嵌入包，会保留许多无信息补丁，且实例特征与类级疾病语义对齐性弱。我们提出概念引导剪枝与表示学习（CGRL）框架，将源自疾病提示的类级概念原型引入MIL流程。首先，概念相关剪枝按与类概念的相似度对补丁实例排序，保留前K个与概念相关的补丁用于下游MIL聚合。其次，概念引导对比表示学习从相同相似度矩阵构建类内正、负补丁集，优化目标类、对称辅助和跨类分离目标，从而规范投影概念空间。我们使用多种代表性MIL方法在TCGA - BRCA和TCGA - NSCLC上评估CGRL。实验结果表明，CGRL改进了多个模型 - 数据集组合，在准确性和宏F1上有显著提升，同时通过概念相关剪枝降低了计算成本。这些发现表明类级语义概念为弱监督计算病理学中的补丁选择和表示学习提供了有效且实用的先验知识。
英文摘要
Weakly supervised whole-slide image (WSI) classification is widely used in computational pathology because slide-level labels are easier to obtain than dense region annotations. Existing multiple instance learning (MIL) methods often aggregate large bags of patch embeddings using mainly visual cues, which can retain many non-informative patches and provide weak alignment between instance features and class-level disease semantics. We propose Concept-Guided Pruning and Representation Learning (CGRL), a simple framework that introduces class-level concept prototypes derived from disease prompts into the MIL pipeline. First, concept-relevance pruning ranks patch instances by their similarity to class concepts and retains the top-K concept-relevant patches for downstream MIL aggregation. Second, concept-guided contrastive representation learning constructs class-wise positive and negative patch sets from the same similarity matrix and optimizes target-class, symmetric auxiliary, and cross-class separation objectives, thereby regularizing the projected concept space. We evaluate CGRL on TCGA-BRCA and TCGA-NSCLC using multiple representative MIL methods. Experimental results show that CGRL improves several model-dataset combinations, with gains depending on the downstream MIL model and dataset. It achieves particularly clear improvements in accuracy and macro-F1 while reducing computational cost through concept-relevance pruning. These findings demonstrate that class-level semantic concepts provide an effective and practical prior for patch selection and representation learning in weakly supervised computational pathology.
