SL-HarDNet: Skin lesion segmentation with HarDNet

doi:10.3389/fbioe.2022.1028690

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	SL-HarDNet: Skin lesion segmentation with HarDNet
	R. Bai and M. Zhou
	2023
发表期刊	Frontiers in Bioengineering and Biotechnology
ISSN	22964185
卷号	10
摘要	Automatic segmentation of skin lesions from dermoscopy is of great significance for the early diagnosis of skin cancer. However, due to the complexity and fuzzy boundary of skin lesions, automatic segmentation of skin lesions is a challenging task. In this paper, we present a novel skin lesion segmentation network based on HarDNet (SL-HarDNet). We adopt HarDNet as the backbone, which can learn more robust feature representation. Furthermore, we introduce three powerful modules, including: cascaded fusion module (CFM), spatial channel attention module (SCAM) and feature aggregation module (FAM). Among them, CFM combines the features of different levels and effectively aggregates the semantic and location information of skin lesions. SCAM realizes the capture of key spatial information. The cross-level features are effectively fused through FAM, and the obtained high-level semantic position information features are reintegrated with the features from CFM to improve the segmentation performance of the model. We apply the challenge dataset ISIC-2016&PH2 and ISIC-2018, and extensively evaluate and compare the state-of-the-art skin lesion segmentation methods. Experiments show that our SL-HarDNet performance is always superior to other segmentation methods and achieves the latest performance. Copyright © 2023 Bai and Zhou.
DOI	10.3389/fbioe.2022.1028690
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收录类别	sci ; ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67333
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	R. Bai and M. Zhou. SL-HarDNet: Skin lesion segmentation with HarDNet[J]. Frontiers in Bioengineering and Biotechnology,2023,10.
APA	R. Bai and M. Zhou.(2023).SL-HarDNet: Skin lesion segmentation with HarDNet.Frontiers in Bioengineering and Biotechnology,10.
MLA	R. Bai and M. Zhou."SL-HarDNet: Skin lesion segmentation with HarDNet".Frontiers in Bioengineering and Biotechnology 10(2023).

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