Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract discriminative feature representations within Whole Slide Images (WSIs) in computational pathology. Despite driving notable progress, existing MIL approaches suffer from limitations in facilitating comprehensive and efficient interactions among instances, as well as challenges related to time-consuming computations and overfitting. In this paper, we incorporate the Selective Scan Space State Sequential Model (Mamba) in Multiple Instance Learning (MIL) for long sequence modeling with linear complexity, termed as MambaMIL. By inheriting the capability of vanilla Mamba, MambaMIL demonstrates the ability to comprehensively understand and perceive long sequences of instances. Furthermore, we propose the Sequence Reordering Mamba (SR-Mamba) aware of the order and distribution of instances, which exploits the inherent valuable information embedded within the long sequences. With the SR-Mamba as the core component, MambaMIL can effectively capture more discriminative features and mitigate the challenges associated with overfitting and high computational overhead. Extensive experiments on two public challenging tasks across nine diverse datasets demonstrate that our proposed framework performs favorably against state-of-the-art MIL methods. The code is released at https://github.com/isyangshu/MambaMIL.

翻译：多实例学习（MIL）已成为计算病理学中全切片图像（WSI）内提取判别性特征表示的主流范式。尽管推动了显著进展，现有MIL方法在促进实例间全面高效交互方面存在局限，同时面临计算耗时和过拟合的挑战。本文引入选择性扫描空间状态序列模型（Mamba）到多实例学习（MIL）中，以实现线性复杂度的长序列建模，并将其命名为MambaMIL。通过继承原始Mamba的能力，MambaMIL展现出全面理解与感知长实例序列的能力。此外，我们提出感知实例顺序与分布的序列重排Mamba（SR-Mamba），其充分利用了长序列中蕴含的内在有价值信息。以SR-Mamba为核心组件，MambaMIL能有效捕获更具判别性的特征，并缓解过拟合与高计算开销相关的挑战。在涵盖九个数据集的两种公开挑战性任务上的大量实验表明，我们的框架性能优于现有最优MIL方法。代码发布于 https://github.com/isyangshu/MambaMIL。