Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

Code

• github.com/HuCaoFighting/Swin-Unet
  OfficialIn paperpytorch★ 2,352
• github.com/yingkaisha/keras-unet-collection
  tf★ 757
• github.com/simonustc/mcpa-for-2d-medical-image-segmentation
  pytorch★ 15
• github.com/L-A-Sandhu/Swin-Unet-
  tf★ 13
• github.com/WonJunPark/swinUNet_custom_training
  pytorch★ 5
• github.com/Arnukk/CASPIAN
  tf★ 3
• github.com/MindSpore-scientific-2/code-4/tree/main/D-Unet_Mindspore
  mindspore★ 0

Abstract

In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis. Especially, the deep neural networks based on U-shaped architecture and skip-connections have been widely applied in a variety of medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global and long-range semantic information interaction well due to the locality of the convolution operation. In this paper, we propose Swin-Unet, which is an Unet-like pure Transformer for medical image segmentation. The tokenized image patches are fed into the Transformer-based U-shaped Encoder-Decoder architecture with skip-connections for local-global semantic feature learning. Specifically, we use hierarchical Swin Transformer with shifted windows as the encoder to extract context features. And a symmetric Swin Transformer-based decoder with patch expanding layer is designed to perform the up-sampling operation to restore the spatial resolution of the feature maps. Under the direct down-sampling and up-sampling of the inputs and outputs by 4x, experiments on multi-organ and cardiac segmentation tasks demonstrate that the pure Transformer-based U-shaped Encoder-Decoder network outperforms those methods with full-convolution or the combination of transformer and convolution. The codes and trained models will be publicly available at https://github.com/HuCaoFighting/Swin-Unet.

Tasks

Benchmark Results

Reproductions