Bai Ruifeng, Zhou Mingwei
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China.
University of Chinese Academy of Sciences, Beijing, China.
Front Bioeng Biotechnol. 2023 Jan 5;10:1028690. doi: 10.3389/fbioe.2022.1028690. eCollection 2022.
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.
从皮肤镜图像中自动分割皮肤病变对于皮肤癌的早期诊断具有重要意义。然而，由于皮肤病变的复杂性和边界模糊性，皮肤病变的自动分割是一项具有挑战性的任务。在本文中，我们提出了一种基于HarDNet的新型皮肤病变分割网络（SL-HarDNet）。我们采用HarDNet作为主干网络，它能够学习更强大的特征表示。此外，我们引入了三个强大的模块，包括：级联融合模块（CFM）、空间通道注意力模块（SCAM）和特征聚合模块（FAM）。其中，CFM结合不同层次的特征，有效地聚合皮肤病变的语义和位置信息。SCAM实现关键空间信息的捕获。通过FAM有效地融合跨层次特征，并将获得的高层次语义位置信息特征与CFM的特征重新整合，以提高模型的分割性能。我们应用了挑战性数据集ISIC-2016&PH2和ISIC-2018，并对当前最先进的皮肤病变分割方法进行了广泛的评估和比较。实验表明，我们的SL-HarDNet性能始终优于其他分割方法，并取得了最新的性能。
