Therefore, this paper proposes a multi-branch decoding model. This model uses Res2Net as backbone and captures features with different receptive fields through ...
We propose a novel Multi-Branch Transformer decoder architecture to aggregate hierarchical features from both CNN and Transformer to provide rich clues for ...
Jun 24, 2023 · This model uses Res2Net as backbone and captures features with different receptive fields through a multi-scale context-awareness module.
Experiments on three medical image datasets demonstrate the generality and robustness of the designed network. The ablation study shows the efficiency and ...
Missing: Decoding | Show results with:Decoding
Jul 1, 2024 · In this paper, we propose an efficient medical image classification network based on an alternating mixture of CNN and Transformer tandem, which ...
Missing: Decoding | Show results with:Decoding
Background: Automatic segmentation of medical images has progressed greatly owing to the development of convolutional neural networks (CNNs).
Sep 25, 2024 · MultiTrans achieves the highest segmentation accuracy on three medical image datasets with different modalities: Synapse, ACDC and M&Ms.
Aug 12, 2024 · In this paper, we have proposed a semi-supervised medical image segmentation method based on the Transformer network. The method utilizes ...
May 24, 2024 · TransUNet [4] represents the pioneering effort to integrate Transformers into medical image segmentation, utilizing a hybrid structure of CNNs ...
A novel medical image segmentation approach by using multi-branch ...
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Apr 25, 2023 · We propose a novel multi-branch medical image segmentation network MBSNet. We first design two branches using a parallel residual mixer (PRM) module and dilate ...