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
loss function, which assists the model in learning from hard examples, while
down-weighting the easy ones. Experiments conducted on a dermatology image
benchmark demonstrate the superiority of our proposed approach over several
standard baseline methods, achieving significant performance improvements.
Interestingly, we show through feature visualization and analysis that our
method leads to context based lesion assessment that can reach an expert
dermatologist level.

本文介绍了一种基于对抗训练和迁移学习的皮肤病变图像自动分类的两阶段框架。通过学习数据分布的内类别映射和利用图像翻译技术，我们能够在缺乏表征病变的样本中生成人工制造的少数不平衡训练样本。实验证明，所提出的方法优于几种标准的基线方法，并具备能够达到皮肤科医师专家水平的基于上下文的病变评估方法。