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.

通过在多实例学习（MIL）中引入选择性扫描空间状态顺序模型（Mamba），提出了具有线性复杂度的长序列建模的MambaMIL。通过继承香草Mamba的能力，MambaMIL能够全面理解和感知实例的长序列，并借助序列重新排序的Mamba（SR-Mamba）有效地捕获更具有区分度的特征，并减轻与过度拟合和计算负担过重相关的挑战。通过对九个不同数据集上的两个公开挑战任务进行的大量实验，我们的提出的框架相对于最先进的MIL方法取得了良好的性能。

MambaMIL: 在计算病理学中通过序列重新排序来增强长序列建模