DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation
arXiv:2310.12570 · doi:10.3389/fbioe.2024.1398237
Abstract
Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional Unet architectures and their transformer-integrated variants excel in automated segmentation tasks. However, they lack the ability to harness the intrinsic position and channel features of image. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block(DA-Block) into the traditional U-shaped architecture. Unlike earlier transformer-based U-net models, DA-TransUNet utilizes Transformers and DA-Block to integrate not only global and local features, but also image-specific positional and channel features, improving the performance of medical image segmentation. By incorporating a DA-Block at the embedding layer and within each skip connection layer, we substantially enhance feature extraction capabilities and improve the efficiency of the encoder-decoder structure. DA-TransUNet demonstrates superior performance in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across multiple datasets. In summary, DA-TransUNet offers a significant advancement in medical image segmentation, providing an effective and powerful alternative to existing techniques. Our architecture stands out for its ability to improve segmentation accuracy, thereby advancing the field of automated medical image diagnostics. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.
References in corpus (8)
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Attention U-Net: Learning Where to Look for the Pancreas
- TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
- The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
- MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation
- ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data
- Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
- RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans