Skin cancer is a serious condition that requires accurate identification and treatment. One way to assist clinicians in this task is by using computer-aided diagnosis (CAD) tools that can automatically segment skin lesions from dermoscopic images. To this end, a new adversarial learning-based framework called EGAN has been developed. This framework uses an unsupervised generative network to generate accurate lesion masks. It consists of a generator module with a top-down squeeze excitation-based compound scaled path and an asymmetric lateral connection-based bottom-up path, and a discriminator module that distinguishes between original and synthetic masks. Additionally, a morphology-based smoothing loss is implemented to encourage the network to create smooth semantic boundaries of lesions. The framework is evaluated on the International Skin Imaging Collaboration (ISIC) Lesion Dataset 2018 and outperforms the current state-of-the-art skin lesion segmentation approaches with a Dice coefficient, Jaccard similarity, and Accuracy of 90.1%, 83.6%, and 94.5%, respectively. This represents a 2% increase in Dice Coefficient, 1% increase in Jaccard Index, and 1% increase in Accuracy.

翻译：皮肤癌是一种需要精确识别与治疗的严重疾病。辅助临床医生完成该任务的方式之一是采用计算机辅助诊断（CAD）工具，该类工具可自动从皮肤镜图像中分割皮肤病变。为此，本研究提出了一种基于对抗学习的新型框架EGAN。该框架利用无监督生成网络生成精确的病变掩膜，其生成器模块包含基于自上而下挤压激励的复合缩放路径与非对称侧向连接的自下而上路径，判别器模块则用于区分原始掩膜与合成掩膜。此外，框架引入基于形态学的平滑损失函数，以促使网络生成病变的平滑语义边界。在国际皮肤成像协作组织（ISIC）2018病变数据集上的评估表明，该框架在Dice系数、Jaccard相似度和准确率上分别达到90.1%、83.6%和94.5%，优于当前最先进的皮肤病变分割方法，其中Dice系数、Jaccard指数和准确率分别提升2%、1%和1%。