mlsanityData to 14 Oct 2026

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation

arXiv 2508.07237 ↗First posted 10 Aug 2025cs.CVcited by 0

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

Abstract (arXiv)

Precise lesion resection depends on accurately identifying fine-grained anatomical structures. While many coarse-grained segmentation (CGS) methods have been successful in large-scale segmentation (e.g., organs), they fall short in clinical scenarios requiring fine-grained segmentation (FGS), which remains challenging due to frequent individual variations in small-scale anatomical structures. Although recent Mamba-based models have advanced medical image segmentation, they often rely on fixed manually-defined scanning orders, which limit their adaptability to individual variations in FGS. To address this, we propose ASM-UNet, a novel Mamba-based architecture for FGS. It introduces adaptive scan scores to dynamically guide the scanning order, generated by combining group-level commonalities and individual-level variations. Experiments on two public datasets (ACDC and Synapse) and a newly proposed challenging biliary tract FGS dataset, namely BTMS, demonstrate that ASM-UNet achieves superior performance in both CGS and FGS tasks. Our code and dataset are available at https://github.com/YqunYang/ASM-UNet.

Key facts. No citing papers in the map yet (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 30 of its 44 references that are in the map. First posted on arXiv 10 Aug 2025.

Builds on

30 of 44 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation12 Mar
  2. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining5 Feb
  3. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentation4 Feb
  4. 2024SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation24 Jan
  5. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation9 Jan

Other references · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  5. 2022A ConvNet for the 2020scited by 3,579
  6. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  7. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  8. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  9. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,056
  10. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  11. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  12. 2020UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentationcited by 332
13 more
  1. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  2. 2020AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?cited by 224
  3. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  4. 2022Medical Image Segmentation Review: The success of U-Netcited by 191
  5. 2021nnFormer: Interleaved Transformer for Volumetric Segmentationcited by 180
  6. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  7. 2021LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentationcited by 85
  8. 2020Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstractioncited by 70
  9. 2017CNN-based Segmentation of Medical Imaging Datacited by 48
  10. 2024nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Modelcited by 45
  11. 2024SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentationcited by 31
  12. 2023COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-trainingcited by 16
  13. 2024MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMscited by 14

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Close in wording, no citation either way

  1. 2024Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentation
  2. 2024MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation
  3. 2025ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation
  4. 2024Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation
  5. 2026Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation
  6. 2025UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation
  7. 2025MM-UNet: Meta Mamba UNet for Medical Image Segmentation
  8. 2026DB-MSMUNet:Dual Branch Multi-scale Mamba UNet for Pancreatic CT Scans Segmentation