Convolutional Neural Networks (CNNs) and Transformer-based self-attention
models have become standard for medical image segmentation. This paper
demonstrates that convolution and self-attention, while widely used, are not
the only effective methods for segmentation. Breaking with convention, we
present a Convolution and self-Attention Free Mamba-based semantic Segmentation
Network named CAF-MambaSegNet. Specifically, we design a Mamba-based Channel
Aggregator and Spatial Aggregator, which are applied independently in each
encoder-decoder stage. The Channel Aggregator extracts information across
different channels, and the Spatial Aggregator learns features across different
spatial locations. We also propose a Linearly Interconnected Factorized Mamba
(LIFM) Block to reduce the computational complexity of a Mamba and to enhance
its decision function by introducing a non-linearity between two factorized
Mamba blocks. Our goal is not to outperform state-of-the-art results but to
show how this innovative, convolution and self-attention-free method can
inspire further research beyond well-established CNNs and Transformers,
achieving linear complexity and reducing the number of parameters. Source code
and pre-trained models will be publicly available.

该论文介绍了一种名为 CAF-MambaSegNet 的基于 Mamba 的的语义分割网络，通过设计用于不同编码器 - 解码器阶段的基于 Mamba 的通道聚合器和空间聚合器，提出了一种在医学图像分割中不使用卷积和自注意力的新方法，以实现线性复杂度和参数数量减少的目标。