mlsanityData to 7 Oct 2026

KM-UNet KAN Mamba UNet for medical image segmentation

arXiv 2501.02559 ↗First posted 5 Jan 2025eess.IV cs.AI cs.CVcited by 0

On the map: State-space sequence models (S4, Mamba) → Mamba for medical image analysis

Abstract (arXiv)

Medical image segmentation is a critical task in medical imaging analysis. Traditional CNN-based methods struggle with modeling long-range dependencies, while Transformer-based models, despite their success, suffer from quadratic computational complexity. To address these limitations, we propose KM-UNet, a novel U-shaped network architecture that combines the strengths of Kolmogorov-Arnold Networks (KANs) and state-space models (SSMs). KM-UNet leverages the Kolmogorov-Arnold representation theorem for efficient feature representation and SSMs for scalable long-range modeling, achieving a balance between accuracy and computational efficiency. We evaluate KM-UNet on five benchmark datasets: ISIC17, ISIC18, CVC, BUSI, and GLAS. Experimental results demonstrate that KM-UNet achieves competitive performance compared to state-of-the-art methods in medical image segmentation tasks. To the best of our knowledge, KM-UNet is the first medical image segmentation framework integrating KANs and SSMs. This work provides a valuable baseline and new insights for the development of more efficient and interpretable medical image segmentation systems. The code is open source at https://github.com/2760613195/KM_UNet Keywords:KAN,Manba, state-space models,UNet, Medical image segmentation, Deep learning

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 27 of its 33 references that are in the map. First posted on arXiv 5 Jan 2025.

Builds on

27 of 33 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentation4 Feb
  2. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation9 Jan

Other references · 25

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  4. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  5. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  6. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  7. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  8. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  9. 2023RWKV: Reinventing RNNs for the Transformer Eracited by 814
  10. 2021Do Vision Transformers See Like Convolutional Neural Networks?cited by 671
  11. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 657
  12. 2022Hungry Hungry Hippos: Towards Language Modeling with State Space Modelscited by 495
13 more
  1. 2017Deep Learning for Medical Image Analysiscited by 399
  2. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 324
  3. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
  4. 2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 176
  5. 2020Medical Image Segmentation Using Deep Learning: A Surveycited by 163
  6. 2022Global Context Vision Transformerscited by 98
  7. 2023White-Box Transformers via Sparse Rate Reductioncited by 77
  8. 2022MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentationcited by 59
  9. 2020Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embeddingcited by 48
  10. 2022TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentationcited by 43
  11. 2017ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detectioncited by 34
  12. 2021Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentationcited by 15
  13. 2021Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translationcited by 10

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  1. 2024LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation
  2. 2024Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation
  3. 2024KAN-Mamba FusionNet: Redefining Medical Image Segmentation with Non-Linear Modeling
  4. 2024MSVM-UNet: Multi-Scale Vision Mamba UNet for Medical Image Segmentation
  5. 2025UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation
  6. 2024HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation
  7. 2025ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation
  8. 2024LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation