Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation

• URL: http://arxiv.org/abs/2302.14450v2
• Date: Wed, 27 Sep 2023 07:56:37 GMT
• Title: Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation
• Authors: Lichao Wang, Jiahao Huang, Xiaodan Xing, Guang Yang
• Abstract summary: We propose to incorporate the Shifted Window (Swin) Deformable Attention into a hybrid architecture to improve segmentation performance. Our proposed Swin Deformable Attention Hybrid UNet (SDAH-UNet) demonstrates state-of-the-art performance on both anatomical and lesion segmentation tasks.
• Score: 3.407509559779547
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Medical image segmentation is a crucial task in the field of medical image analysis. Harmonizing the convolution and multi-head self-attention mechanism is a recent research focus in this field, with various combination methods proposed. However, the lack of interpretability of these hybrid models remains a common pitfall, limiting their practical application in clinical scenarios. To address this issue, we propose to incorporate the Shifted Window (Swin) Deformable Attention into a hybrid architecture to improve segmentation performance while ensuring explainability. Our proposed Swin Deformable Attention Hybrid UNet (SDAH-UNet) demonstrates state-of-the-art performance on both anatomical and lesion segmentation tasks. Moreover, we provide a direct and visual explanation of the model focalization and how the model forms it, enabling clinicians to better understand and trust the decision of the model. Our approach could be a promising solution to the challenge of developing accurate and interpretable medical image segmentation models.

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