We propose to self-distill a Transformer-based UNet for medical image segmentation, which simultaneously learns global semantic information and local spatial- ...
Overview of the proposed MISSU. First, the Transformer module puts after 3D local features to model the global features with long-range dependency. Multi-scale ...
This work proposes to self-distill a Transformer-based UNet for medical image segmentation, which simultaneously learns global semantic information and local ...
The input X initially undergoes 3 pretrained ResNet50 blocks to generate generic features and extract spatial information. To enhance the segmentation ...
Jun 16, 2024 · Although a number of methods have been shown promising segmentation results, such as UNETR [10], TransUNet [11], MISSU [12] , TransFuse and MedT ...
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2023. MISSU: 3D medical image segmentation via self-distilling TransUNet. N Wang, S Lin, X Li, K Li, Y Shen, Y Gao, L Ma. IEEE transactions on medical imaging ...
MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet · 1 code ... Towards Compact Single Image Super-Resolution via Contrastive Self-distillation.
Oct 28, 2024 · In 2023, Wang N et al. developed a 3D Medical Image Segmentation via Self-Distilling TransUNet, termed MISSU, which could achieve efficient 3D ...
The proposed model is extensively experimented on seven medical image segmentation datasets including polyp segmentation to demonstrate its efficacy. Comparison ...
This work proposes a Global-Local representation learning net for medical image segmentation, namely GL-Segnet, and exploits the structural similarity between ...