Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation
This addresses the need for accurate and interpretable segmentation models in clinical settings, though it is incremental as it builds on existing hybrid architectures.
The paper tackled the problem of limited interpretability in hybrid convolution and self-attention models for medical image segmentation by proposing Swin Deformable Attention Hybrid U-Net (SDAH-UNet), which achieved state-of-the-art performance on anatomical and lesion segmentation tasks while providing visual explanations for model decisions.
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.