标题： SemaMIL：基于检索引导状态空间建模的语义重排序用于全幻灯片图像分类 显示英文标题

标题： SemaMIL: Semantic Reordering with Retrieval-Guided State Space Modeling for Whole Slide Image Classification

摘要： 多实例学习（MIL）已成为计算病理学中从全切片图像（WSIs）中提取判别特征的领先方法。基于注意力的MIL方法可以识别关键区域，但容易忽略上下文关系。Transformer模型能够建模交互作用，但需要二次计算成本，并且容易过拟合。状态空间模型（SSMs）具有线性复杂度，然而打乱区域顺序会破坏组织学意义并降低可解释性。在本工作中，我们引入了SemaMIL，它集成了语义重排序（SR），一种通过可逆排列将语义相似的区域聚类并按顺序排列的自适应方法，以及一个语义引导检索状态空间模块（SRSM），该模块选择代表性查询子集以调整状态空间参数，从而改善全局建模。在四个WSI亚型数据集上的评估表明，与强基线相比，SemaMIL在更少的FLOPs和参数下实现了最先进的准确率。 显示英文摘要

摘要： Multiple instance learning (MIL) has become the leading approach for extracting discriminative features from whole slide images (WSIs) in computational pathology. Attention-based MIL methods can identify key patches but tend to overlook contextual relationships. Transformer models are able to model interactions but require quadratic computational cost and are prone to overfitting. State space models (SSMs) offer linear complexity, yet shuffling patch order disrupts histological meaning and reduces interpretability. In this work, we introduce SemaMIL, which integrates Semantic Reordering (SR), an adaptive method that clusters and arranges semantically similar patches in sequence through a reversible permutation, with a Semantic-guided Retrieval State Space Module (SRSM) that chooses a representative subset of queries to adjust state space parameters for improved global modeling. Evaluation on four WSI subtype datasets shows that, compared to strong baselines, SemaMIL achieves state-of-the-art accuracy with fewer FLOPs and parameters.