MSCA-Net: Multi-scale contextual attention network for skin lesion segmentation

• Published in: Pattern recognition Vol. 139; p. 109524
• Main Authors: Sun, Yongheng, Dai, Duwei, Zhang, Qianni, Wang, Yaqi, Xu, Songhua, Lian, Chunfeng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Global-local channel spatial attention module; Multi-scale bridge module; Scale-aware deep supervision module; Skin lesion segmentation; Global-local channel spatial attention module; Skin lesion segmentation; Multi-scale bridge module; Scale-aware deep supervision module
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2023.109524

•A novel deep learning framework MSCA-Net for skin lesion segmentation.•A MSB module to integrate multi-scale features of the encoder.•A GL-CSAM module to capture global contextual information with four attentions.•A SADS module to integrate multi-scale features of the decoder to improve the output.•Extensive experiments and analysis confirm the superiority of the pro posed MSCA-Net. Lesion segmentation algorithms automatically outline lesion areas in medical images, facilitating more effective identification and assessment of the clinically relevant features, and improving the efficacy and diagnosis accuracy. However, most fully convolutional network based segmentation methods suffer from spatial and contextual information loss when decreasing image resolution. To overcome this shortcoming, this paper proposes a skin lesion segmentation model, namely, the Multi-Scale Contextual Attention Network (MSCA-Net), which can exploit the multi-scale contextual information in images. Inspired by the skip connection of U-Net, we design a multi-scale bridge (MSB) module which interacts with multi-scale features to effectively fuse the multi-scale contextual information of the encoder and decoder path features. We further propose a global-local channel spatial attention module (GL-CSAM), aiming at capturing global contextual information. In addition, to take full advantage of the multi-scale features of the decoder, we propose a scale-aware deep supervision (SADS) module to achieve hierarchical iterative deep supervision. Comprehensive experimental results on the public dataset of ISIC 2017, ISIC 2018, and PH2 show that our proposed method outperforms other state-of-the-art methods, demonstrating the efficacy of our method in skin lesion segmentation. Our code is available at https://github.com/YonghengSun1997/MSCA-Net.