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.

本文研究了实现强健的皮肤病诊断的实用方法，并提出一种以皮损为病变类型标签的自动化诊断系统作为目标的方法，并应用卷积神经网络（CNNs）来实现疾病定向诊断和病变定向分类的分类器，并采用来自六个公开皮肤病学大全的 75665 个皮肤病图像来进行训练与测试，结果证明基于病变定向分类的分类器的效果优于疾病定向的分类器。