Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation

• URL: http://arxiv.org/abs/2210.16898v1
• Date: Sun, 30 Oct 2022 17:41:35 GMT
• Title: Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation
• Authors: Ehsan Khodapanah Aghdam, Reza Azad, Maral Zarvani, Dorit Merhof
• Abstract summary: We propose Att-SwinU-Net, an attention-based Swin U-Net extension, for medical image segmentation. We argue that the classical concatenation operation utilized in the skip connection path can be further improved by incorporating an attention mechanism.
• Score: 4.320393382724066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Melanoma is caused by the abnormal growth of melanocytes in human skin. Like other cancers, this life-threatening skin cancer can be treated with early diagnosis. To support a diagnosis by automatic skin lesion segmentation, several Fully Convolutional Network (FCN) approaches, specifically the U-Net architecture, have been proposed. The U-Net model with a symmetrical architecture has exhibited superior performance in the segmentation task. However, the locality restriction of the convolutional operation incorporated in the U-Net architecture limits its performance in capturing long-range dependency, which is crucial for the segmentation task in medical images. To address this limitation, recently a Transformer based U-Net architecture that replaces the CNN blocks with the Swin Transformer module has been proposed to capture both local and global representation. In this paper, we propose Att-SwinU-Net, an attention-based Swin U-Net extension, for medical image segmentation. In our design, we seek to enhance the feature re-usability of the network by carefully designing the skip connection path. We argue that the classical concatenation operation utilized in the skip connection path can be further improved by incorporating an attention mechanism. By performing a comprehensive ablation study on several skin lesion segmentation datasets, we demonstrate the effectiveness of our proposed attention mechanism.

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