JAAL-Net: a joint attention and adversarial learning network for skin lesion segmentation. (21st April 2023)

• Record Type: Journal Article
• Authors: Xiong, Siyu; Pan, Lili; Lei, Qianhui; Ma, Junyong; Shao, Weizhi; Beckman, Eric
• Is Part Of: Physics in medicine & biology. Volume 68:Number 8(2023)
• Publication Date: 2023-04-21
• Subjects: skin lesion segmentation -- melanoma -- contour attention -- adversarial learning -- medical image processing; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/acc630
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Objective. Skin lesion segmentation plays an important role in the diagnosis and treatment of melanoma. Existing skin lesion segmentation methods have trouble distinguishing hairs, air bubbles, and blood vessels around lesions, which affects the segmentation performance. Approach. To clarify the lesion boundary and raise the accuracy of skin lesion segmentation, a joint attention and adversarial learning network (JAAL-Net) is proposed that consists of a generator and a discriminator. In the JAAL-Net, the generator is a local fusion network (LF-Net) utilizing the encoder-decoder structure. The encoder contains a convolutional block attention module to increase the weight of lesion information. The decoder involves a contour attention to obtain edge information and locate the lesion. To aid the LF-Net generate higher confidence predictions, a discriminant dual attention network is constructed with channel attention and position attention. Main results. The JAAL-Net is evaluated on three datasets ISBI2016, ISBI2017 and ISIC2018. The intersection over union of the JAAL-Net on the three datasets are 90.27%, 89.56% and 80.76%, respectively. Experimental results show that the JAAL-Net obtains rich lesion and boundary information, enhances the confidence of the predictions, and improves the accuracy of skin lesion segmentation. Significance. The proposed approach effectively improves the performance of the model for skin lesion segmentation, which can assist physicians in accurate diagnosis well.