  In the field of biomedical image analysis, the quest for architectures
capable of effectively capturing long-range dependencies is paramount,
especially when dealing with 3D image segmentation, classification, and
landmark detection. Traditional Convolutional Neural Networks (CNNs) struggle
with locality respective field, and Transformers have a heavy computational
load when applied to high-dimensional medical images.In this paper, we
introduce nnMamba, a novel architecture that integrates the strengths of CNNs
and the advanced long-range modeling capabilities of State Space Sequence
Models (SSMs). Specifically, we propose the Mamba-In-Convolution with
Channel-Spatial Siamese learning (MICCSS) block to model the long-range
relationship of the voxels. For the dense prediction and classification tasks,
we also design the channel-scaling and channel-sequential learning methods.
Extensive experiments on 6 datasets demonstrate nnMamba's superiority over
state-of-the-art methods in a suite of challenging tasks, including 3D image
segmentation, classification, and landmark detection. nnMamba emerges as a
robust solution, offering both the local representation ability of CNNs and the
efficient global context processing of SSMs, setting a new standard for
long-range dependency modeling in medical image analysis. Code is available at
https://github.com/lhaof/nnMamba
