Recent sequence modeling approaches using Selective State Space Sequence
Models, referred to as Mamba models, have seen a surge of interest. These
models allow efficient processing of long sequences in linear time and are
rapidly being adopted in a wide range of applications such as language
modeling, demonstrating promising performance. To foster their reliable use in
real-world scenarios, it is crucial to augment their transparency. Our work
bridges this critical gap by bringing explainability, particularly Layer-wise
Relevance Propagation (LRP), to the Mamba architecture. Guided by the axiom of
relevance conservation, we identify specific components in the Mamba
architecture, which cause unfaithful explanations. To remedy this issue, we
propose MambaLRP, a novel algorithm within the LRP framework, which ensures a
more stable and reliable relevance propagation through these components. Our
proposed method is theoretically sound and excels in achieving state-of-the-art
explanation performance across a diverse range of models and datasets.
Moreover, MambaLRP facilitates a deeper inspection of Mamba architectures,
uncovering various biases and evaluating their significance. It also enables
the analysis of previous speculations regarding the long-range capabilities of
Mamba models.

借助相关性传播的层次和 Mamba 架构的分析，我们提出了 MambaLRP 算法，以增强深度学习模型的可解释性，并取得了多个模型和数据集上的最先进解释性能。

MambaLRP: 解释选择性状态空间序列模型

MambaLRP: Explaining Selective State Space Sequence Models

Sequence modeling plays a vital role across various domains, with recurrent
neural networks being historically the predominant method of performing these
tasks. However, the emergence of transformers has altered this paradigm due to
their superior performance. Built upon these advances, transformers have
conjoined CNNs as two leading foundational models for learning visual
representations. However, transformers are hindered by the $\mathcal{O}(N^2)$
complexity of their attention mechanisms, while CNNs lack global receptive
fields and dynamic weight allocation. State Space Models (SSMs), specifically
the \textit{\textbf{Mamba}} model with selection mechanisms and hardware-aware
architecture, have garnered immense interest lately in sequential modeling and
visual representation learning, challenging the dominance of transformers by
providing infinite context lengths and offering substantial efficiency
maintaining linear complexity in the input sequence. Capitalizing on the
advances in computer vision, medical imaging has heralded a new epoch with
Mamba models. Intending to help researchers navigate the surge, this survey
seeks to offer an encyclopedic review of Mamba models in medical imaging.
Specifically, we start with a comprehensive theoretical review forming the
basis of SSMs, including Mamba architecture and its alternatives for sequence
modeling paradigms in this context. Next, we offer a structured classification
of Mamba models in the medical field and introduce a diverse categorization
scheme based on their application, imaging modalities, and targeted organs.
Finally, we summarize key challenges, discuss different future research
directions of the SSMs in the medical domain, and propose several directions to
fulfill the demands of this field. In addition, we have compiled the studies
discussed in this paper along with their open-source implementations on our
GitHub repository.

该论文通过对 Mamba 模型在医学图像中的应用进行全面回顾，介绍了该模型的架构、分类和未来研究方向。