Anomaly Detection for Skin Disease Images Using Variational Autoencoder

Understand

In this paper, we demonstrate the potential of applying Variational Autoencoder (VAE) [10] for anomaly detection in skin disease images.

• VAE is a class of deep generative models which is trained by maximizing the evidence lower bound of data distribution [10].
• When trained on only normal data, the resulting model is able to perform efficient inference and to determine if a test image is normal or not.
• We perform experiments on ISIC2018 Challenge Disease Classification dataset (Task 3) and compare different methods to use VAE to detect anomaly.