A latent space exploration for microscopic skin lesion augmentations with VQ-VAE-2 and PixelSNAIL

Skin cancer affects more than 3 million people only in the US. Comprehensive microscopic databases include around 30 thousand samples, limiting the richness of patterns that can be presented to machine learning. To this end, generative models such as GANs have been proposed for creating realistic synthetic images but, despite their popularity, they are often difficult to train and control. Recently an autoregressive approach based on a quantized autoencoder showed state of the art performances while being simple to train and provide synthetic data generation opportunities. In the first part of this paper we evaluate the training of VQ-VAE-2 with different latent space configuration. In the second part, we show how to use a learned prior over the latent space with PixelSNAIL to generate and modify skin lesions. We show how this process can be used for powerful data augmentation and visualization for skin health, evaluating it on a downstream application that classifies malignant lesions