华东理工大学学报（自然科学版）2024，Vol.50Issue(1)：97-105,9.DOI:10.14135/j.cnki.1006-3080.20221108002
基于密集多尺度特征和双注意力模块的皮肤病变分割
Skin Lesion Segmentation Based on Dense Multi-Scale Features and Dual Attention Module
摘要
Abstract
Aiming at the problems of varying sizes,shapes,internal pixel differences,blurry boundaries,and presence of bubbles in skin lesion segmentation,a U-shaped segmentation network,DDAnet,based on dense multi-scale features and dual attention module is proposed.The DenseASPP module in this network obtains rich multi-scale information by densely connecting multiple atrous convolution layers.Meanwhile,the dual attention module composed of CAM and PAM encodes global contextual information to re-register feature maps on channels and positions,achieving emphasis on relevant features and suppression of irrelevant features.These two modules are connected in parallel and work together to improve segmentation performance.On the ISIC2018 dataset,the Acc,JI,DC,Sen and Spec index values of DDAnet are 96.75%,85.00%,91.36%,91.82%,and 97.42%,respectively.The segmentation results are better than those of other segmentation networks,and for extremely challenging cases,DDAnet can still produce accurate and reliable segmentation results,indicating its potential to assist doctors in skin lesion segmentation in clinical diagnosis.关键词
皮肤病变分割/DenseASPP模块/CAM/PAM/双注意力模块Key words
skin lesion segmentation/DenseASPP module/CAM/PAM/dual attention module分类
信息技术与安全科学引用本文复制引用
费承,罗健旭..基于密集多尺度特征和双注意力模块的皮肤病变分割[J].华东理工大学学报（自然科学版）,2024,50(1):97-105,9.基金项目
上海市科技创新行动计划(19511121203) （19511121203）
