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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