  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.
