EfficientMIL：用于WSI分类的高效线性复杂度MIL方法
EfficientMIL: Efficient Linear-Complexity MIL Method for WSI Classification
AI总结：
针对现有基于注意力的MIL方法处理WSI时计算复杂度高的问题，提出搭配APS模块的线性复杂度方法EfficientMIL，用高效序列模型替代自注意力，在多数据集上准确率与AUC均超SOTA且计算效率显著提升。
摘要：
全切片图像（WSI）分类是计算病理学中的一项核心挑战，多示例学习（MIL）已成为该领域的主流范式。当前最先进（SOTA）的MIL方法依赖注意力机制，虽能取得良好性能，但处理数十万张图像块时因复杂度为二次方，需要大量计算资源。为解决这一计算瓶颈，我们提出EfficientMIL，一种用于WSI分类的新型线性复杂度MIL方法，搭配我们设计的图像块选择模块自适应图像块选择器（APS），用基于RNN的GRU、LSTM以及状态空间模型（SSM）Mamba等高效序列模型，替代基于Transformer的MIL方法中二次复杂度的自注意力机制。EfficientMIL在大幅提升计算效率的同时，在多个组织病理学数据集上的表现优于其他MIL方法。在TCGA-Lung数据集上，EfficientMIL-Mamba取得了0.976的AUC和0.933的准确率；在CAMELYON16数据集上，EfficientMIL-GRU取得了0.990的AUC和0.975的准确率，超越了此前的最先进方法。大量实验表明，APS在图像块选择方面也比传统选择策略更有效。
英文摘要：
Whole slide images (WSIs) classification represents a fundamental challenge in computational pathology, where multiple instance learning (MIL) has emerged as the dominant paradigm. Current state-of-the-art (SOTA) MIL methods rely on attention mechanisms, achieving good performance but requiring substantial computational resources due to quadratic complexity when processing hundreds of thousands of patches. To address this computational bottleneck, we introduce EfficientMIL, a novel linear-complexity MIL approach for WSIs classification with the patches selection module Adaptive Patch Selector (APS) that we designed, replacing the quadratic-complexity self-attention mechanisms in Transformer-based MIL methods with efficient sequence models including RNN-based GRU, LSTM, and State Space Model (SSM) Mamba. EfficientMIL achieves significant computational efficiency improvements while outperforming other MIL methods across multiple histopathology datasets. On TCGA-Lung dataset, EfficientMIL-Mamba achieved AUC of 0.976 and accuracy of 0.933, while on CAMELYON16 dataset, EfficientMIL-GRU achieved AUC of 0.990 and accuracy of 0.975, surpassing previous state-of-the-art methods. Extensive experiments demonstrate that APS is also more effective for patches selection than conventional selection strategies.
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