Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem. Also, skin lesion images are largely similar in overall appearance owing to the low inter-class variability. In this paper, we propose a two-stage framework for automatic classification of skin lesion images using adversarial training and transfer learning toward melanoma detection. In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis by learning the inter-class mapping and synthesizing under-represented class samples from the over-represented ones using unpaired image-to-image translation. In the second stage, we train a deep convolutional neural network for skin lesion classification using the original training set combined with the newly synthesized under-represented class samples. The training of this classifier is carried out by minimizing the focal lo...

中文翻译：

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使用对抗训练和深度转移学习来检测黑素瘤。

皮肤病变数据集主要由正常样本组成，只有少量的异常样本组成，这引起了类别失衡问题。同样，由于低的类间差异性，皮肤病变图像在整体外观上非常相似。在本文中，我们提出了一个两阶段的框架，用于通过对抗训练和将学习转移到黑素瘤检测来对皮肤病变图像进行自动分类。在第一阶段，我们通过学习类间映射并使用不成对的图像到图像从过度表示的样本中合成不足表示的样本样本，来利用数据分布的类别间变化进行条件图像合成的任务翻译。在第二阶段 我们使用原始训练集结合新合成的代表性不足的样本来训练用于皮肤病变分类的深度卷积神经网络。该分类器的训练是通过最小化焦距来进行的。