Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation

• URL: http://arxiv.org/abs/2603.12547v1
• Date: Fri, 13 Mar 2026 01:13:52 GMT
• Title: Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation
• Abstract summary: We propose a decoder-centric approach for generalized 2D medical image segmentation.<n>The proposed Deco-Mamba follows a U-Net-like structure with a Transformer-CNN-Mamba design.<n>Experiments on diverse medical image segmentation benchmarks yield state-of-the-art performance and strong generalization capability.
• Score: 14.622519786493188
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning has achieved remarkable success in medical image segmentation, often reaching expert-level accuracy in delineating tumors and tissues. However, most existing approaches remain task-specific, showing strong performance on individual datasets but limited generalization across diverse imaging modalities. Moreover, many methods focus primarily on the encoder, relying on large pretrained backbones that increase computational complexity. In this paper, we propose a decoder-centric approach for generalized 2D medical image segmentation. The proposed Deco-Mamba follows a U-Net-like structure with a Transformer-CNN-Mamba design. The encoder combines a CNN block and Transformer backbone for efficient feature extraction, while the decoder integrates our novel Co-Attention Gate (CAG), Vision State Space Module (VSSM), and deformable convolutional refinement block to enhance multi-scale contextual representation. Additionally, a windowed distribution-aware KL-divergence loss is introduced for deep supervision across multiple decoding stages. Extensive experiments on diverse medical image segmentation benchmarks yield state-of-the-art performance and strong generalization capability while maintaining moderate model complexity. The source code will be released upon acceptance.

Related papers

• MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation [5.111086106563699]
  Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into pixel-level predictions.<n>Recent advances in large-scale pretraining and transformer-based encoders have substantially improved feature extraction.<n>We propose a context-aware gated decoder that regulates feature fusion and contextual aggregation throughout the decoding process.
  arXiv  Detail & Related papers  (2026-07-01T04:07:20Z)
• Optimizing Medical Image Segmentation with Advanced Decoder Design [0.8402155549849591]
  U-Net is widely used in medical image segmentation due to its simple and flexible architecture design. We propose Swin DER (i.e., Swin UNETR Decoder Enhanced and Refined) by specifically optimizing the design of these three components. Our model design achieves excellent results, surpassing other state-of-the-art methods on both the Synapse and the MSD brain tumor segmentation task.
  arXiv  Detail & Related papers  (2024-10-05T11:47:13Z)
• AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ Segmentation [31.97835089989928]
  Medical image segmentation is a crucial task in computer vision, supporting clinicians in diagnosis, treatment planning, and disease monitoring.<n>We propose the Adaptive Semantic Network (ASSNet), a transformer architecture that effectively integrates local and global features for precise medical image segmentation.<n>Tests on diverse medical image segmentation tasks, including multi-organ, liver tumor, and bladder tumor segmentation, demonstrate that ASSNet achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2024-09-12T06:25:44Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [66.89632406480949]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image.<n>Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet [55.16833099336073]
  We propose to self-distill a Transformer-based UNet for medical image segmentation. It simultaneously learns global semantic information and local spatial-detailed features. Our MISSU achieves the best performance over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-02T07:38:53Z)
• Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation [63.46694853953092]
  Swin-Unet is an Unet-like pure Transformer for medical image segmentation. tokenized image patches are fed into the Transformer-based U-shaped decoder-Decoder architecture.
  arXiv  Detail & Related papers  (2021-05-12T09:30:26Z)
• Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images [1.8146155083014204]
  This paper proposes a novel algorithm for automated vertebrae segmentation via 3D volumetric spine CT images. The proposed model is based on the structure of encoder to decoder, using layer normalization to optimize mini-batch training performance. The experimental results show that our model achieves competitive performance compared with other state-of-the-art medical semantic segmentation methods.
  arXiv  Detail & Related papers  (2021-04-08T12:09:16Z)
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation [78.01570371790669]
  Medical image segmentation is an essential prerequisite for developing healthcare systems. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the de-facto standard. We propose TransUNet, which merits both Transformers and U-Net, as a strong alternative for medical image segmentation.
  arXiv  Detail & Related papers  (2021-02-08T16:10:50Z)
• Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers [149.78470371525754]
  We treat semantic segmentation as a sequence-to-sequence prediction task. Specifically, we deploy a pure transformer to encode an image as a sequence of patches. With the global context modeled in every layer of the transformer, this encoder can be combined with a simple decoder to provide a powerful segmentation model, termed SEgmentation TRansformer (SETR) SETR achieves new state of the art on ADE20K (50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on Cityscapes.
  arXiv  Detail & Related papers  (2020-12-31T18:55:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.