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Hybrid Transformer-Mamba is a promising foundational deep model for medical image segmentation. Existing hybrid models are constructed by empirically interleaving Transformer and Mamba blocks, which fail to mitigate the feature biases between different deep models and lack a comprehensive understanding of local, long-range, and longer-range features in medical images. To address these issues, this paper proposes a novel hybrid model with U-Net architecture, called TM-UNet. The encoder of TM-UNet is implemented using hybrid CNN-Transformer-Mamba (CTM) blocks. Each CTM block comprises two parallel Transformer branches and CNN-Mamba branch, interacted by a dual cross-attention module. The CTM block achieves more effective multi-hierarchical feature representation than standard cascading structure, owing to its parallel branch design and dual cross-attention mechanism. Unlike typical U-Net with symmetrical encoder-decoder, the decoder of TM-UNet is implemented using convolutional layer and cascaded fusion module. Additionally, an attention based feature fusion block is developed to self-adjust features for final prediction. Extensive experiments on the ISIC2018, BUSI, Kvasir-SEG, and LiTS datasets demonstrate the superiority of TM-UNet. Specifically, TM-UNet achieves 1.07%/1.68% Dice/IoU improvements over SETR-DTMFormer on the Kvasir-SEG dataset. The source code will be available on https://github.com/yongchangxu/TM-Unet .

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Transformer-Mamba U-Net With Dual Cross-Attention for Medical Image Segmentation

  • 会议论文

  • 发表日期：

    2026年

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  • Yongchang Xu,
  • Haitao Yin

摘要

Hybrid Transformer-Mamba is a promising foundational deep model for medical image segmentation. Existing hybrid models are constructed by empirically interleaving Transformer and Mamba blocks, which fail to mitigate the feature biases between different deep models and lack a comprehensive understanding of local, long-range, and longer-range features in medical images. To address these issues, this paper proposes a novel hybrid model with U-Net architecture, called TM-UNet. The encoder of TM-UNet is implemented using hybrid CNN-Transformer-Mamba (CTM) blocks. Each CTM block comprises two parallel Transformer branches and CNN-Mamba branch, interacted by a dual cross-attention module. The CTM block achieves more effective multi-hierarchical feature representation than standard cascading structure, owing to its parallel branch design and dual cross-attention mechanism. Unlike typical U-Net with symmetrical encoder-decoder, the decoder of TM-UNet is implemented using convolutional layer and cascaded fusion module. Additionally, an attention based feature fusion block is developed to self-adjust features for final prediction. Extensive experiments on the ISIC2018, BUSI, Kvasir-SEG, and LiTS datasets demonstrate the superiority of TM-UNet. Specifically, TM-UNet achieves 1.07%/1.68% Dice/IoU improvements over SETR-DTMFormer on the Kvasir-SEG dataset. The source code will be available on https://github.com/yongchangxu/TM-Unet .