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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 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRI

    cs.CV 2026-07 conditional novelty 6.0 of 10

    HieraSample, a Mamba-based frequency-hierarchical active sampler, matches fully-sampled ACL diagnosis AUC at 4-10x acceleration on fastMRI+ knee MRI.

  2. Seeing It Before It Happens: In-Generation NSFW Detection for Diffusion-Based Text-to-Image Models

    cs.CV 2025-08 unverdicted novelty 6.0 of 10

    No verifiable result can be extracted because the body is a different paper on medical image fusion, not the NSFW detection study described in the abstract.

  3. ViTaL: A Multimodality Dataset and Benchmark for Multi-pathological Ovarian Tumor Recognition

    eess.IV 2025-07 conditional novelty 6.0 of 10

    ViTaL provides 496 patients' ultrasound images, lab tables, and reports for six-class ovarian tumor recognition, with a cross-attention fusion model reaching 85.59% accuracy.

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

    eess.IV 2025-07 conditional novelty 6.0 of 10

    GAST-Mamba, a Mamba-based network with a spatial-temporal gate, improves simulated and qualitative in vivo MRF T1/T2 reconstruction over SCQ, LG-ViT, CONV-ICA, and MRF-Mixer.

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

    cs.CV 2025-07 conditional novelty 5.0 of 10

    QuarterMap prunes spatial activations before VMamba's four-directional scan and upsamples after, yielding up to 1.11x throughput with under 1% accuracy loss on ImageNet classification.

  6. Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation

    cs.CV 2026-07 accept novelty 4.0 of 10

    A comprehensive review synthesizing AI-driven sperm analysis across computer vision tasks, multimodal fusion, and a staged clinical translation roadmap.

  7. Nexus-INR: Diverse Knowledge-guided Arbitrary-Scale Multimodal Medical Image Super-Resolution

    eess.IV 2025-08 unverdicted novelty 4.0 of 10

    Nexus-INR combines arbitrary-scale implicit-neural super-resolution, cross-modal knowledge distillation, and joint segmentation to improve multimodal brain MRI reconstruction and tumor segmentation.

  8. Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography

    eess.IV 2025-07 conditional novelty 4.0 of 10

    Mammo-Mamba, a gated MambaVision model with sequential mixture-of-expert-style depth routing, reports 0.8696 accuracy and 0.9089 AUC on the CBIS-DDSM mass classification benchmark.

  9. 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.

  10. 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.

  11. FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images

    cs.CV 2025-06 conditional novelty 4.0 of 10

    FMaMIL combines Mamba-based multiple instance learning with learnable frequency-domain encoding and CAM-guided pseudo-label refinement to segment lesions from image-level labels only.

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

    cs.CV 2025-06 reject novelty 4.0 of 10

    A diffusion image restoration model with a Mamba condition network reports low LPIPS/FID on several benchmarks, but the PSNR losses and internal inconsistencies undermine the stated performance claims.

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

    cs.LG 2025-09 reject novelty 3.0 of 10

    SSM/Mamba long-range dependency decays exponentially with the time gap by construction; a proposed interaction-based hidden state update can break this decay, but its proven stability covers only a restrictive special case.

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