Abstract
Skin disease is a quite common disease of human beings, which has been found in all races and ages. It seriously affects people’s quality of life or even endangers people’s lives. In this paper, we propose a large-scale, Asian-dominated dataset of skin diseases with bounding box labels, namely XiangyaDerm. It contains 107,565 clinical images, covering 541 types of skin diseases. Each image in this dataset is labeled by professional doctors. As far as we know, this dataset is the largest clinical image dataset of Asian skin diseases used in Computer Aided Diagnosis (CAD) system worldwide. We compare the classification results of several advanced Convolutional Neural Networks (CNNs) on this dataset. InceptionResNetV2 is the best one for 80 skin disease classification whose Top-1 and Top-3 accuracies can reach 0.588 and 0.764, which proves the usefulness of the proposed benchmark dataset, and gives the baseline performance on it. The cross-test experiment with Derm101 shows us that the CNN model has a very different test effect on different ethnic datasets. Therefore, to build a skin disease CAD system with high performance and stability, we recommend to establish a specific dataset of skin diseases for different regions and races.
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Xie, B. et al. (2019). XiangyaDerm: A Clinical Image Dataset of Asian Race for Skin Disease Aided Diagnosis. In: Zhou, L., et al. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention. LABELS HAL-MICCAI CuRIOUS 2019 2019 2019. Lecture Notes in Computer Science(), vol 11851. Springer, Cham. https://doi.org/10.1007/978-3-030-33642-4_3
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