nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model
<|reference_start|>nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model: 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<|reference_end|>
@article{gong2024nnmamba:,
    title={nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark
  Detection with State Space Model},
    author={Haifan Gong, Luoyao Kang, Yitao Wang, Xiang Wan, Haofeng Li},
    journal={arXiv preprint arXiv:2402.03526},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03526},
    primaryClass={cs.CV}
}