In the realm of dermatological diagnoses, where the analysis of dermatoscopic and microscopic skin lesion images is pivotal for the accurate and early detection of various medical conditions, the costs associated with creating diverse and high-quality annotated datasets have hampered the accuracy and generalizability of machine learning models. We propose an innovative unsupervised augmentation solution that harnesses Generative Adversarial Network (GAN) based models and associated techniques over their latent space to generate controlled semiautomatically-discovered semantic variations in dermatoscopic images. We created synthetic images to incorporate the semantic variations and augmented the training data with these images. With this approach, we were able to increase the performance of machine learning models and set a new benchmark amongst non-ensemble based models in skin lesion classification on the HAM10000 dataset; and used the observed analytics and generated models for detailed studies on model explainability, affirming the effectiveness of our solution.

本研究解决了皮肤病学诊断中高质量注释数据集不足的问题，提出了一种创新的无监督增强解决方案，利用生成对抗网络（GAN）模型在潜在空间中生成皮肤镜图像的语义变体。通过合成图像增加训练数据，我们提高了机器学习模型的性能，并在HAM10000数据集中设定了新的基准，验证了我们方案的有效性和模型可解释性。

基于生成对抗网络和闭式因子分解的皮肤镜图像合成生成