XiangyaDerm: A Clinical Image Dataset of Asian Race for Skin Disease Aided Diagnosis

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.

[1]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[2]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[3]  R. Hay,et al.  The global burden of skin disease in 2010: an analysis of the prevalence and impact of skin conditions. , 2014, The Journal of investigative dermatology.

[4]  Wei-Chung Cheng,et al.  Consistency and Standardization of Color in Medical Imaging: a Consensus Report , 2014, Journal of Digital Imaging.

[5]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Haofu Liao,et al.  A Deep Learning Approach to Universal Skin Disease Classification , 2015 .

[7]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Jiebo Luo,et al.  Skin disease classification versus skin lesion characterization: Achieving robust diagnosis using multi-label deep neural networks , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[9]  Kai Wang,et al.  A Benchmark for Automatic Visual Classification of Clinical Skin Disease Images , 2016, ECCV.

[10]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[11]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[12]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Paul L. Rosin,et al.  Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[14]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[15]  Johan Debayle,et al.  Automatic classification of skin lesions using color mathematical morphology-based texture descriptors , 2015, International Conference on Quality Control by Artificial Vision.

[16]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.