Dual Adversarial Autoencoder for Dermoscopic image Generative Modeling

Skin cancer is a severe public health issue in the United States and worldwide. While Computer Aided Diagnosis (CAD) of dermoscopic images shows potential in accelerating diagnosis and improving accuracy, numerous issues remain that may be addressed by generative modeling. Major challenges in automated skin lesion classification include manual efforts required to label new training data and a relatively limited amount of data compared to more generalized computer vision tasks. We propose a novel generative model based on a dual discrimination training algorithm for autoencoders. At each training iteration, the encoder and decoder undergo two stages of adversarial training by two individual discriminator networks. The algorithm is end-to-end trainable with standard back-propagation. In contrast with traditional autoencoders, our method incorporates extra constraints via adversarial training, which results in visually realistic synthetic data. We demonstrate the versatility of the proposed method and applications on numerous tasks including latent space visualization, data augmentation, and image denoising.