While recent years have witnessed the remarkable success of deep learning methods in automated skin lesion detection systems, there still exists a gap between manual assessment of experts and automated evaluation of computers. The reason behind such a gap is the deep learning models demand considerable amounts of data, while the availability of annotated images is often limited. Data Augmentation (DA) is one way to mitigate the lack of labeled data; however, the augmented images intrinsically have a similar distribution to the original ones, leading to limited performance improvement. To satisfy the data lack in the real image distribution, we synthesize skin lesion images – realistic but completely different from the original ones – using Generative Adversarial Networks (GANs). In this paper, we propose the Self-attention Progressive Growing of GANs (SPGGANs) to generate fine-grained 256 × 256 skin lesion images for Convolutional Neural Network-based melanoma detection, which is challenging via conventional GANs; difficulties arise due to unstable GAN training with high resolution and a variety of skin lesions in size, shape, and location. In SPGGAN, details can be generated using aggregated information from all feature locations. Moreover, the discriminator can monitor that highly detailed features in distant portions of the image are consistent with each other. Furthermore, the Two-Timescale Update Rule (TTUR) is applied to SPGGAN (SPGGAN-TTUR) to improve stability while generating 256 × 256 skin lesion images. SPGGAN-TTUR is evaluated on data generation and classification tasks using the HAM10000 dataset. Our results confirm the importance of our proposed GAN-based DA approach for training skin lesion classifiers and indicate that it can lead to statistically significant improvements ($p$-value $<0.05$) in the sensitivity (recall) over non-augmented and augmented, with classical DA, counterparts. In general, in the case of all classes, The sensitivity improvements were 5.6% and 2.5% over non-augmented and augmented (with the best DA scheme) counterparts, respectively. Specifically, in the case of melanoma class, the sensitivity improvements were 13.8% and 8.6%. We believe that the proposed approach can be adopted in clinical practice to improve the sensitivity of automated skin lesion detection in dermoscopic images and thus support dermatologists’ efforts to improve melanoma diagnosis.

尽管近年来目睹了深度学习方法在自动皮肤病变检测系统中的巨大成功，但在专家的手动评估和计算机的自动评估之间仍然存在差距。这种差距背后的原因是深度学习模型需要大量数据，而带注释的图像的可用性通常受到限制。数据增强（DA）是减轻标签数据不足的一种方法。然而，增强图像本质上具有与原始图像相似的分布，从而导致有限的性能改进。为了满足实际图像分布中缺少的数据，我们使用生殖对抗网络（GAN）合成了皮肤病变图像-逼真但与原始图像完全不同。在本文中，我们提出了GAN（SPGGAN）的自注意渐进生长，以生成细化的256×256皮肤病变图像，用于基于卷积神经网络的黑色素瘤检测，这是传统GAN所面临的挑战；由于具有高分辨率的不稳定GAN训练以及大小，形状和位置的各种皮肤损伤，导致出现困难。在SPGGAN中，可以使用来自所有要素位置的汇总信息生成详细信息。而且，鉴别器可以监视图像的远处部分中的高度详细的特征彼此一致。此外，将两时标更新规则（TTUR）应用于SPGGAN（SPGGAN-TTUR），以提高稳定性，同时生成256×256皮肤病变图像。使用HAM10000数据集对SPGGAN-TTUR进行了数据生成和分类任务评估。$p$-值 $<0。05$）（相对于非增强和增强）（经典DA）的敏感性（召回率）。通常，在所有类别的情况下，其灵敏度分别比未增强和增强（采用最佳DA方案）的同类分别提高了5.6％和2.5％。具体而言，在黑色素瘤类别的情况下，灵敏度提高了13.8％和8.6％。我们认为，该提议的方法可在临床实践中采用，以提高皮肤镜图像中自动皮肤病变检测的敏感性，从而支持皮肤科医生改善黑素瘤诊断的努力。