  In this study, we investigate what a practically useful approach is in order
to achieve robust skin disease diagnosis. A direct approach is to target the
ground truth diagnosis labels, while an alternative approach instead focuses on
determining skin lesion characteristics that are more visually consistent and
discernible. We argue that, for computer-aided skin disease diagnosis, it is
both more realistic and more useful that lesion type tags should be considered
as the target of an automated diagnosis system such that the system can first
achieve a high accuracy in describing skin lesions, and in turn facilitate
disease diagnosis using lesion characteristics in conjunction with other
evidence. To further meet such an objective, we employ convolutional neural
networks (CNNs) for both the disease-targeted and lesion-targeted
classifications. We have collected a large-scale and diverse dataset of 75,665
skin disease images from six publicly available dermatology atlantes. Then we
train and compare both disease-targeted and lesion-targeted classifiers,
respectively. For disease-targeted classification, only 27.6% top-1 accuracy
and 57.9% top-5 accuracy are achieved with a mean average precision (mAP) of
0.42. In contrast, for lesion-targeted classification, we can achieve a much
higher mAP of 0.70.
