DMA‐Net: A dual branch encoder and multi‐scale cross attention fusion network for skin lesion segmentation
Abstract Automatic segmentation of skin lesion is an important step in computer‐aided diagnosis. However, due to the significant variations in the size and shape of the lesion areas, as well as the low contrast with normal skin tissue, the boundaries are not clearly distinguishable, leading to a hig...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Image Processing |
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| Online Access: | https://doi.org/10.1049/ipr2.13265 |
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| _version_ | 1846121443204530176 |
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| author | Guangyao Zhai Guanglei Wang Qinghua Shang Yan Li Hongrui Wang |
| author_facet | Guangyao Zhai Guanglei Wang Qinghua Shang Yan Li Hongrui Wang |
| author_sort | Guangyao Zhai |
| collection | DOAJ |
| description | Abstract Automatic segmentation of skin lesion is an important step in computer‐aided diagnosis. However, due to the significant variations in the size and shape of the lesion areas, as well as the low contrast with normal skin tissue, the boundaries are not clearly distinguishable, leading to a high possibility of incorrect segmentation. Therefore, this task is highly challenging. To overcome these difficulties, this paper proposes a medical image segmentation architecture named dual branch encoder and multi‐scale cross attention fusion network, which includes a dual‐branch encoder based on convolutional neural network and an improved channel‐enhanced Mamba to comprehensively extract local and global information from dermoscopy images. Additionally, to enhance the feature interaction and fusion of local and global information, a multi‐scale cross attention fusion module is adopted to cross‐merge features in different directions and at different scales, maximizing the advantages of the dual‐branch encoder and achieving precise segmentation of skin lesions. Extensive experiments are conducted on three public skin lesion datasets: ISIC‐2018, ISIC‐2017, and ISIC‐2016, to verify the effectiveness and superiority of the proposed method. The dice similarity coefficient scores on the three datasets reached 81.77%, 81.68% and 85.60%, respectively, surpassing most state‐of‐the‐art methods. |
| format | Article |
| id | doaj-art-67d379026c1f40fcb2eb8d53f867b295 |
| institution | Kabale University |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-67d379026c1f40fcb2eb8d53f867b2952024-12-16T04:00:31ZengWileyIET Image Processing1751-96591751-96672024-12-0118144531454110.1049/ipr2.13265DMA‐Net: A dual branch encoder and multi‐scale cross attention fusion network for skin lesion segmentationGuangyao Zhai0Guanglei Wang1Qinghua Shang2Yan Li3Hongrui Wang4College of Electronic and Information Engineering Hebei University Hebei ChinaCollege of Electronic and Information Engineering Hebei University Hebei ChinaCollege of Electronic and Information Engineering Hebei University Hebei ChinaCollege of Electronic and Information Engineering Hebei University Hebei ChinaCollege of Electronic and Information Engineering Hebei University Hebei ChinaAbstract Automatic segmentation of skin lesion is an important step in computer‐aided diagnosis. However, due to the significant variations in the size and shape of the lesion areas, as well as the low contrast with normal skin tissue, the boundaries are not clearly distinguishable, leading to a high possibility of incorrect segmentation. Therefore, this task is highly challenging. To overcome these difficulties, this paper proposes a medical image segmentation architecture named dual branch encoder and multi‐scale cross attention fusion network, which includes a dual‐branch encoder based on convolutional neural network and an improved channel‐enhanced Mamba to comprehensively extract local and global information from dermoscopy images. Additionally, to enhance the feature interaction and fusion of local and global information, a multi‐scale cross attention fusion module is adopted to cross‐merge features in different directions and at different scales, maximizing the advantages of the dual‐branch encoder and achieving precise segmentation of skin lesions. Extensive experiments are conducted on three public skin lesion datasets: ISIC‐2018, ISIC‐2017, and ISIC‐2016, to verify the effectiveness and superiority of the proposed method. The dice similarity coefficient scores on the three datasets reached 81.77%, 81.68% and 85.60%, respectively, surpassing most state‐of‐the‐art methods.https://doi.org/10.1049/ipr2.13265codecsimage segmentationskin |
| spellingShingle | Guangyao Zhai Guanglei Wang Qinghua Shang Yan Li Hongrui Wang DMA‐Net: A dual branch encoder and multi‐scale cross attention fusion network for skin lesion segmentation IET Image Processing codecs image segmentation skin |
| title | DMA‐Net: A dual branch encoder and multi‐scale cross attention fusion network for skin lesion segmentation |
| title_full | DMA‐Net: A dual branch encoder and multi‐scale cross attention fusion network for skin lesion segmentation |
| title_fullStr | DMA‐Net: A dual branch encoder and multi‐scale cross attention fusion network for skin lesion segmentation |
| title_full_unstemmed | DMA‐Net: A dual branch encoder and multi‐scale cross attention fusion network for skin lesion segmentation |
| title_short | DMA‐Net: A dual branch encoder and multi‐scale cross attention fusion network for skin lesion segmentation |
| title_sort | dma net a dual branch encoder and multi scale cross attention fusion network for skin lesion segmentation |
| topic | codecs image segmentation skin |
| url | https://doi.org/10.1049/ipr2.13265 |
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