  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. The model is able to detect all diseases with 0.779
AUCROC. If we focus on specific diseases, the model is able to detect melanoma
with 0.864 AUCROC and detect actinic keratosis with 0.872 AUCROC, even if it
only sees the images of nevus. To the best of our knowledge, this is the first
applied work of deep generative models for anomaly detection in dermatology.
