  Skin conditions are reported the 4th leading cause of nonfatal disease burden
worldwide. However, given the colossal spectrum of skin disorders defined
clinically and shortage in dermatology expertise, diagnosing skin conditions in
a timely and accurate manner remains a challenging task. Using computer vision
technologies, a deep learning system has proven effective assisting clinicians
in image diagnostics of radiology, ophthalmology and more. In this paper, we
propose a deep learning system (DLS) that may predict differential diagnosis of
skin conditions using clinical images. Our DLS formulates the differential
diagnostics as a multi-label classification task over 80 conditions when only
incomplete image labels are available. We tackle the label incompleteness
problem by combining a classification network with a Graph Convolutional
Network (GCN) that characterizes label co-occurrence and effectively
regularizes it towards a sparse representation. Our approach is demonstrated on
136,462 clinical images and concludes that the classification accuracy greatly
benefit from the Co-occurrence supervision. Our DLS achieves 93.6% top-5
accuracy on 12,378 test images and consistently outperform the baseline
classification network.
