DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT(2022)
摘要
Automatic medical image segmentation has made great progress owing to powerful deep representation learning. Inspired by the success of self-attention mechanism in transformer, considerable efforts are devoted to designing the robust variants of the encoder-decoder architecture with transformer. However, the patch division used in the existing transformer-based models usually ignores the pixel-level intrinsic structural features inside each patch. In this article, we propose a novel deep medical image segmentation framework called dual swin transformer U-Net (DS-TransUNet), which aims to incorporate the hierarchical swin transformer into both the encoder and the decoder of the standard U-shaped architecture. Our DS-TransUNet benefits from the self-attention computation in swin transformer and the designed dual-scale encoding, which can effectively model the non-local dependencies and multiscale contexts for enhancing the semantic segmentation quality of varying medical images. Unlike many prior transformer-based solutions, the proposed DS-TransUNet adopts a well-established dual-scale encoding mechanism that uses dual-scale encoders based on swin transformer to extract the coarse and fine-grained feature representations of different semantic scales. Meanwhile, a well-designed transformer interactive fusion (TIF) module is proposed to effectively perform multiscale information fusion through the self-attention mechanism. Furthermore, we introduce the swin transformer block into the decoder to further explore the long-range contextual information during the up-sampling process. Extensive experiments across four typical tasks for medical image segmentation demonstrate the effectiveness of DS-TransUNet, and our approach significantly outperforms the state-of-the-art methods.
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关键词
Transformers, Image segmentation, Semantics, Decoding, Computer architecture, Task analysis, Medical diagnostic imaging, Hierarchical swin transformer, long-range contextual information, medical image segmentation, transformer interactive fusion~(TIF) module
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