A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network
With the rise of deep learning technology, the field of medical image segmentation has undergone rapid development. In recent years, convolutional neural networks (CNNs) have brought many achievements and become the consensus in medical image segmentation tasks. Although many neural networks based o...
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10194499/ |
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| author | Shuang Liu Zeng Zhuang Yanfeng Zheng Simon Kolmanic |
| author_facet | Shuang Liu Zeng Zhuang Yanfeng Zheng Simon Kolmanic |
| author_sort | Shuang Liu |
| collection | DOAJ |
| description | With the rise of deep learning technology, the field of medical image segmentation has undergone rapid development. In recent years, convolutional neural networks (CNNs) have brought many achievements and become the consensus in medical image segmentation tasks. Although many neural networks based on U-shaped structures and methods, such as skip connections have achieved excellent results in medical image segmentation tasks, the properties of convolutional operations limit their ability to effectively learn local and global features. To address this problem, the Transformer from the field of natural language processing (NLP) was introduced to the image segmentation field. Various Transformer-based networks have shown significant performance advantages over mainstream neural networks in different visual tasks, demonstrating the huge potential of Transformers in the field of image segmentation. However, Transformers were originally designed for NLP and ignore the multidimensional nature of images. In the process of operation, they may destroy the 2D structure of the image and cannot effectively capture low-level features. Therefore, we propose a new multi-scale cross-attention method called M-VAN Unet, which is designed based on the Visual Attention Network (VAN) and can effectively learn local and global features. We propose two attention mechanisms, namely MSC-Attention and LKA-Cross-Attention, for capturing low-level features and promoting global information interaction. MSC-Attention is designed for multi-scale channel attention, while LKA-Cross-Attention is a cross-attention mechanism based on the large kernel attention (LKA). Extensive experiments show that our method outperforms current mainstream methods in evaluation metrics such as Dice coefficient and Hausdorff 95 coefficient. |
| format | Article |
| id | doaj-art-3b2b88f60bfc433bb71dbf5b5441dbb8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3b2b88f60bfc433bb71dbf5b5441dbb82025-01-30T00:00:28ZengIEEEIEEE Access2169-35362023-01-0111819538196410.1109/ACCESS.2023.329882610194499A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation NetworkShuang Liu0https://orcid.org/0000-0002-0095-4328Zeng Zhuang1https://orcid.org/0009-0000-0679-7741Yanfeng Zheng2Simon Kolmanic3School of Computer Science and Engineering, Dalian Minzu University, Liaoning, Dalian, ChinaSchool of Computer Science and Engineering, Dalian Minzu University, Liaoning, Dalian, ChinaSchool of Computer Science and Engineering, Dalian Minzu University, Liaoning, Dalian, ChinaFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaWith the rise of deep learning technology, the field of medical image segmentation has undergone rapid development. In recent years, convolutional neural networks (CNNs) have brought many achievements and become the consensus in medical image segmentation tasks. Although many neural networks based on U-shaped structures and methods, such as skip connections have achieved excellent results in medical image segmentation tasks, the properties of convolutional operations limit their ability to effectively learn local and global features. To address this problem, the Transformer from the field of natural language processing (NLP) was introduced to the image segmentation field. Various Transformer-based networks have shown significant performance advantages over mainstream neural networks in different visual tasks, demonstrating the huge potential of Transformers in the field of image segmentation. However, Transformers were originally designed for NLP and ignore the multidimensional nature of images. In the process of operation, they may destroy the 2D structure of the image and cannot effectively capture low-level features. Therefore, we propose a new multi-scale cross-attention method called M-VAN Unet, which is designed based on the Visual Attention Network (VAN) and can effectively learn local and global features. We propose two attention mechanisms, namely MSC-Attention and LKA-Cross-Attention, for capturing low-level features and promoting global information interaction. MSC-Attention is designed for multi-scale channel attention, while LKA-Cross-Attention is a cross-attention mechanism based on the large kernel attention (LKA). Extensive experiments show that our method outperforms current mainstream methods in evaluation metrics such as Dice coefficient and Hausdorff 95 coefficient.https://ieeexplore.ieee.org/document/10194499/CNNsdeep learningmedical image processingNLPsemantic segmentation |
| spellingShingle | Shuang Liu Zeng Zhuang Yanfeng Zheng Simon Kolmanic A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network IEEE Access CNNs deep learning medical image processing NLP semantic segmentation |
| title | A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network |
| title_full | A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network |
| title_fullStr | A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network |
| title_full_unstemmed | A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network |
| title_short | A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network |
| title_sort | van based multi scale cross attention mechanism for skin lesion segmentation network |
| topic | CNNs deep learning medical image processing NLP semantic segmentation |
| url | https://ieeexplore.ieee.org/document/10194499/ |
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