mlsanityData to 7 Oct 2026

When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentation

arXiv 2411.09363 ↗First posted 14 Nov 2024eess.IVcited by 0

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

Abstract (arXiv)

Automatic melanoma segmentation is essential for early skin cancer detection, yet challenges arise from the heterogeneity of melanoma, as well as interfering factors like blurred boundaries, low contrast, and imaging artifacts. While numerous algorithms have been developed to address these issues, previous approaches have often overlooked the need to jointly capture spatial and sequential features within dermatological images. This limitation hampers segmentation accuracy, especially in cases with indistinct borders or structurally similar lesions. Additionally, previous models lacked both a global receptive field and high computational efficiency. In this work, we present the xLSTM-VMUNet Model, which jointly capture spatial and sequential features within dermatological images successfully. xLSTM-VMUNet can not only specialize in extracting spatial features from images, focusing on the structural characteristics of skin lesions, but also enhance contextual understanding, allowing more effective handling of complex medical image structures. Experiment results on the ISIC2018 dataset demonstrate that xLSTM-VMUNet outperforms VMUNet by 4.85% on DSC and 6.41% on IoU on the ISIC2017 dataset, by 1.25% on DSC and 2.07% on IoU on the ISIC2018 dataset, with faster convergence and consistently high segmentation performance. Our code is available at https://github.com/FangZhuoyi/XLSTM-VMUNet.

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 67 of its 107 references that are in the map. First posted on arXiv 14 Nov 2024.

Builds on

67 of 107 references.

Closest ancestors · 10

Same direction, newest first

  1. 2024A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond3 Oct
  2. 2024xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart1 Jul
  3. 2024UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation29 Mar
  4. 2024H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation20 Mar
  5. 2024VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation14 Mar
  6. 2024LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation8 Mar
  7. 2024Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation7 Feb
  8. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining5 Feb
  9. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentation4 Feb
  10. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation9 Jan

Other references · 57

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2023Segment Anythingcited by 8,158
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  7. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
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  9. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  10. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  11. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  12. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
38 more
  1. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  2. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  3. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
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  6. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 657
  7. 2017Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)cited by 549
  8. 2015An Introduction to Convolutional Neural Networkscited by 510
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  13. 2022Diagonal State Spaces are as Effective as Structured State Spacescited by 350
  14. 2024xLSTM: Extended Long Short-Term Memorycited by 320
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  16. 2022Long Range Language Modeling via Gated State Spacescited by 265
  17. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 260
  18. 2024LocalMamba: Visual State Space Model with Windowed Selective Scancited by 235
  19. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
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  30. 2024Vision-LSTM: xLSTM as Generic Vision Backbonecited by 51
  31. 2024A Survey on Visual Mambacited by 50
  32. 2024nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Modelcited by 45
  33. 2023CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objectscited by 43
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  37. 2022A Survey on Deep Learning for Skin Lesion Segmentationcited by 22
  38. 2022A survey, review, and future trends of skin lesion segmentation and classificationcited by 20

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  2. 2025UltraLBM-UNet: Ultralight Bidirectional Mamba-based Model for Skin Lesion Segmentation
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  4. 2025ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation
  5. 2026Bridging Research and Practice: A Systematic Evaluation of Generalist and Dermatology-Specific Models in Clinical Skin Lesion Classification
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