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Found 2 papers, 2 papers with code
TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation
Accurate segmentation of organs or lesions from medical images is crucial for reliable diagnosis of diseases and organ morphometry.
DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation
To alleviate these problems, we propose a novel deep medical image segmentation framework called Dual Swin Transformer U-Net (DS-TransUNet), which might be the first attempt to concurrently incorporate the advantages of hierarchical Swin Transformer into both encoder and decoder of the standard U-shaped architecture to enhance the semantic segmentation quality of varying medical images.
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