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Skin disease classification versus skin lesion characterization: Achieving robust diagnosis using multi-label deep neural networks

机译：皮肤疾病分类与皮肤病变特征：使用多标签深度神经网络实现可靠的诊断

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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 evidences. To further meet such an objective, we employ convolutional neutral 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.
机译：在这项研究中，我们调查什么是一种实用的方法，以实现可靠的皮肤疾病诊断。一种直接的方法是针对地面真相诊断标签，而另一种方法则侧重于确定在视觉上更一致和可辨别的皮肤病变特征。我们认为，对于计算机辅助性皮肤疾病的诊断，将病变类型标签视为自动化诊断系统的目标，以使该系统首先能够在描述皮肤病变方面实现高精度，这既现实又有用。进而利用病变特征结合其他证据促进疾病诊断。为了进一步实现这一目标，我们将卷积神经网络（CNN）用于以疾病为目标和以病变为目标的分类。我们已经从六个公开的皮肤病学大西洋地区收集了75665个皮肤疾病图像的大规模且多样化的数据集。然后，我们分别训练和比较以疾病为目标和以病灶为目标的分类器。对于以疾病为目标的分类，仅以平均平均准确度（mAP）为0.42才能达到27.6％的top-1准确度和57.9％的top-5准确度。相反，对于以病灶为目标的分类，我们可以实现0.70的更高的mAP。

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