  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.

Image and Video Processing;Computer Vision and Pattern Recognition