SLT-Net: A codec network for skin lesion segmentation

被引:16
作者
Feng, Kaili [1 ]
Ren, Lili [2 ]
Wang, Guanglei [1 ]
Wang, Hongrui [1 ]
Li, Yan [1 ]
机构
[1] Hebei Univ, Sch Elect Informat Engn, Baoding 071002, Hebei, Peoples R China
[2] Hebei Univ, Affiliated Hosp, Baoding 071030, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
Skin lesion segmentation; Convolutional neural networks; Transformer; Skip connection; BORDER DETECTION; IMAGE; TRANSFORMER;
D O I
10.1016/j.compbiomed.2022.105942
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Automatic segmentation of skin lesions is beneficial for improving the accuracy and efficiency of melanoma diagnosis. However, due to variation in the size and shape of the lesion areas and the low contrast between the edges of the lesion and the normal skin tissue, this task is very challenging. The traditional convolutional neural network based on codec structure lacks the capability of multi-scale context information modeling and cannot realize information interaction of skip connections at the various levels, which limits the segmentation perfor-mance. Therefore, a new codec structure of skin lesion Transformer network (SLT-Net) was proposed and applied to skin lesion segmentation in this study. Specifically, SLT-Net used CSwinUnet as the codec to model the long-distance dependence between features and used the multi-scale context Transformer (MCT) as the skip connection to realize information interaction between skip connections across levels in the channel dimension. We have performed extensive experiments to verify the effectiveness and superiority of our proposed method on three public skin lesion datasets, including the ISIC-2016, ISIC-2017, and ISIC-2018. The DSC values on the three data sets reached 90.45%, 79.87% and 82.85% respectively, higher than most of the state-of-the-art methods. The excellent performance of SLT-Net on these three datasets proved that it could improve the accuracy of skin lesion segmentation, providing a new benchmark reference for skin lesion segmentation tasks. The code is available at https://github.com/FengKaili-fkl/SLT-Net.git.
引用
收藏
页数:11
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