EAAC-Net: An Efficient Adaptive Attention and Convolution Fusion Network for Skin Lesion Segmentation.
Accurate segmentation of skin lesions in dermoscopic images is of key importance for quantitative analysis of melanoma. Although existing medical image segmentation methods significantly improve skin lesion segmentation, they still have limitations in extracting local features with global informatio...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 1120 - 1137 |
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| Main Authors: | , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Apr2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184081730&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081730 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081730 184081730 184081730 10.1007/s10278-024-01223-6 184081730 ppf: 1120 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: EAAC-Net: An Efficient Adaptive Attention and Convolution Fusion Network for Skin Lesion Segmentation. aug: au: Fan, Chao Zhu, Zhentong Peng, Bincheng Xuan, Zhihui Zhu, Xinru affil: https://ror.org/05sbgwt55 School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou City, Henan Province, China sug: subj: Skin Neoplasms Pathology Dermoscopy Convolutional Neural Networks Melanoma Pathology Medical Informatics Human Comparative Studies Descriptive Statistics Algorithms Image Interpretation, Computer Assisted Neural Networks (Computer) Information Science Deep Learning ab: Accurate segmentation of skin lesions in dermoscopic images is of key importance for quantitative analysis of melanoma. Although existing medical image segmentation methods significantly improve skin lesion segmentation, they still have limitations in extracting local features with global information, do not handle challenging lesions well, and usually have a large number of parameters and high computational complexity. To address these issues, this paper proposes an efficient adaptive attention and convolutional fusion network for skin lesion segmentation (EAAC-Net). We designed two parallel encoders, where the efficient adaptive attention feature extraction module (EAAM) adaptively establishes global spatial dependence and global channel dependence by constructing the adjacency matrix of the directed graph and can adaptively filter out the least relevant tokens at the coarse-grained region level, thus reducing the computational complexity of the self-attention mechanism. The efficient multiscale attention-based convolution module (EMA⋅C) utilizes multiscale attention for cross-space learning of local features extracted from the convolutional layer to enhance the representation of richly detailed local features. In addition, we designed a reverse attention feature fusion module (RAFM) to enhance the effective boundary information gradually. To validate the performance of our proposed network, we compared it with other methods on ISIC 2016, ISIC 2018, and PH2 public datasets, and the experimental results show that EAAC-Net has superior segmentation performance under commonly used evaluation metrics. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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