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MedMamba: Vision Mamba for Medical Image Classification

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arxiv 2403.03849 v5 pith:2Q46GRH6 submitted 2024-03-06 eess.IV cs.CVcs.LG

Yubiao Yue , Zhenzhang Li This is my paper · ORCID
classification eess.IVcs.CVcs.LG
keywords medmambaclassificationimagemedicalcomputationalmambavisiondependencies
verification ladder T0 review T1 audit T2 compute T3 formal
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Since the era of deep learning, convolutional neural networks (CNNs) and vision transformers (ViTs) have been extensively studied and widely used in medical image classification tasks. Unfortunately, CNN's limitations in modeling long-range dependencies result in poor classification performances. In contrast, ViTs are hampered by the quadratic computational complexity of their self-attention mechanism, making them difficult to deploy in real-world settings with limited computational resources. Recent studies have shown that state space models (SSMs) represented by Mamba can effectively model long-range dependencies while maintaining linear computational complexity. Inspired by it, we proposed MedMamba, the first Vision Mamba for generalized medical image classification. Concretely, we introduced a novel hybrid basic block named SS-Conv-SSM, which purely integrates the convolutional layers for extracting local features with the abilities of SSM to capture long-range dependencies, aiming to model medical images from different image modalities efficiently. By employing the grouped convolution strategy and channel-shuffle operation, MedMamba successfully provides fewer model parameters and a lower computational burden for efficient applications without sacrificing accuracy. We thoroughly evaluated MedMamba using 16 datasets containing ten imaging modalities and 411,007 images. Experimental results show that MedMamba demonstrates competitive performance on most tasks compared with the state-of-the-art methods. This work aims to explore the potential of Vision Mamba and establish a new baseline for medical image classification, thereby providing valuable insights for developing more powerful Mamba-based artificial intelligence algorithms and applications in medicine. The source codes and all pre-trained weights of MedMamba are available at https://github.com/YubiaoYue/MedMamba.

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Cited by 15 Pith papers

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  4. Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting

    eess.IV 2025-07 conditional novelty 6.0 of 10

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  5. QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models

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  6. A Foundational EDM2-Based Generative Model for High-Resolution Synthetic Fetal Ultrasound Imaging from Open Datasets

    eess.IV 2026-08 conditional novelty 4.0 of 10

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    eess.IV 2025-08 unverdicted novelty 4.0 of 10

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    eess.IV 2025-07 conditional novelty 4.0 of 10

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  10. A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion

    eess.IV 2025-07 reject novelty 4.0 of 10

    A hybrid CNN-VSSM multi-view multi-task model is reported to outperform CNN and VSSM baselines on mammography BI-RADS and diagnostic classification, but without an independent test set.

  11. MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation

    cs.CV 2025-06 conditional novelty 4.0 of 10

    MS-UMamba, a hybrid CNN-Mamba U-Net with an attention-based fusion module, reports mIoU 67.62 and mDice 79.82, exceeding VM-UNet and other baselines on a private fetal ultrasound dataset.

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  13. ControlMambaIR: Conditional Controls with State-Space Model for Image Restoration

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  14. Rethinking the long-range dependency in Mamba/SSM and transformer models

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    cs.CV 2025-02 conditional novelty 1.0 of 10

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