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- W4322760022 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." @default.
- W4322760022 created "2023-03-03" @default.
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- W4322760022 date "2023-02-28" @default.
- W4322760022 modified "2023-09-30" @default.
- W4322760022 title "Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation" @default.
- W4322760022 doi "https://doi.org/10.48550/arxiv.2302.14450" @default.
- W4322760022 hasPublicationYear "2023" @default.
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