mlsanityData to 14 Oct 2026

AC-MAMBASEG: An adaptive convolution and Mamba-based architecture for enhanced skin lesion segmentation

arXiv 2405.03011 ↗First posted 5 May 2024cs.CV cs.AIcited by 6

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

Abstract (arXiv)

Skin lesion segmentation is a critical task in computer-aided diagnosis systems for dermatological diseases. Accurate segmentation of skin lesions from medical images is essential for early detection, diagnosis, and treatment planning. In this paper, we propose a new model for skin lesion segmentation namely AC-MambaSeg, an enhanced model that has the hybrid CNN-Mamba backbone, and integrates advanced components such as Convolutional Block Attention Module (CBAM), Attention Gate, and Selective Kernel Bottleneck. AC-MambaSeg leverages the Vision Mamba framework for efficient feature extraction, while CBAM and Selective Kernel Bottleneck enhance its ability to focus on informative regions and suppress background noise. We evaluate the performance of AC-MambaSeg on diverse datasets of skin lesion images including ISIC-2018 and PH2; then compare it against existing segmentation methods. Our model shows promising potential for improving computer-aided diagnosis systems and facilitating early detection and treatment of dermatological diseases. Our source code will be made available at: https://github.com/vietthanh2710/AC-MambaSeg.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 28 of its 45 references that are in the map. First posted on arXiv 5 May 2024.

Builds on

28 of 45 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation29 Mar
  2. 2024LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation8 Mar
  3. 2024Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation16 Feb
  4. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentation4 Feb
  5. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation9 Jan

Other references · 23

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  5. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  6. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  7. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  8. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
  9. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  10. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  11. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
  12. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,056
11 more
  1. 2024VMamba: Visual State Space Modelcited by 969
  2. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 669
  3. 2019Selective Kernel Networkscited by 403
  4. 2017Tversky loss function for image segmentation using 3D fully convolutional deep networkscited by 228
  5. 2024State Space Models for Event Camerascited by 75
  6. 2024MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selectioncited by 40
  7. 2019DSNet: Automatic Dermoscopic Skin Lesion Segmentationcited by 22
  8. 2024Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learningcited by 22
  9. 2024HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classificationcited by 14
  10. 2024MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye trackingcited by 8
  11. 2023Cross-CBAM: A Lightweight network for Scene Segmentationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 2.

6 papers.

  1. 2024Mamba in Vision: A Comprehensive Survey of Techniques and Applicationscited by 18
  2. 2024OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentationcited by 3
  3. 2024MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentationcited by 2
  4. 2025Prompt-Guided Dual-Path UNet with Mamba for Medical Image Segmentationcited by 0
  5. 2026APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigmscited by 0
  6. 2026An Ultra-Widefield Swept-Source OCTA Dataset and a Polar-Gated Mamba Network for Retinal Vessel Segmentationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation
  2. 2025ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation
  3. 2024MUCM-Net: A Mamba Powered UCM-Net for Skin Lesion Segmentation
  4. 2024SkinMamba: A Precision Skin Lesion Segmentation Architecture with Cross-Scale Global State Modeling and Frequency Boundary Guidance
  5. 2024HC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation
  6. 2025UltraLBM-UNet: Ultralight Bidirectional Mamba-based Model for Skin Lesion Segmentation
  7. 2024When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentation
  8. 2024S3-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation