Skin cancer is a serious condition that requires accurate diagnosis and treatment. One way to assist clinicians in this task is using computer-aided diagnosis (CAD) tools that automatically segment skin lesions from dermoscopic images. We propose a novel adversarial learning-based framework called Efficient-GAN (EGAN) that 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, an asymmetric lateral connection-based bottom-up path, and a discriminator module that distinguishes between original and synthetic masks. A morphology-based smoothing loss is also 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. It 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. We also design a lightweight segmentation framework (MGAN) that achieves comparable performance as EGAN but with an order of magnitude lower number of training parameters, thus resulting in faster inference times for low compute resource settings.

翻译：皮肤癌是一种需要精确诊断与治疗的严重疾病。辅助临床医生的方式之一是使用计算机辅助诊断（CAD）工具，从皮肤镜图像中自动分割病变区域。我们提出一种名为Efficient-GAN（EGAN）的新型对抗学习框架，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和基于非对称侧向连接的自下而上路径，同时采用判别器模块区分真实掩膜与合成掩膜。此外，我们还引入基于形态学的平滑损失函数，以促使网络生成平滑的病变语义边界。该框架在国际皮肤成像协作组织（ISIC）2018年病变数据集上进行评估，其Dice系数、Jaccard相似度和准确率分别达到90.1%、83.6%和94.5%，超越了当前最先进的皮肤病变分割方法。我们还设计了一个轻量级分割框架（MGAN），该框架在保持与EGAN相当性能的同时，训练参数量降低一个数量级，从而在低计算资源环境下实现更快的推理速度。