University of Waterloo skin cancer database
Introduced by Venugopal et al. in DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images
The dataset is maintained by VISION AND IMAGE PROCESSING LAB, University of Waterloo. The images of the dataset were extracted from the public databases DermIS and DermQuest, along with manual segmentations of the lesions.
The dataset was used in the following journal publication. [1] Glaister, J., A. Wong, and D. A. Clausi, "Automatic segmentation of skin lesions from dermatological photographs using a joint probabilistic texture distinctiveness approach", IEEE Transactions on Biomedical Engineering [2] Amelard, R., J. Glaister, A. Wong, and D. A. Clausi, "High-level intuitive features (HLIFs) for intuitive skin lesion descriptionpdf", IEEE Transactions on Biomedical Engineering, vol. 62, issue 3, pp. 820-831, October, 2015. [3] Glaister, J., R. Amelard, A. Wong, and D. A. Clausi, "MSIM: Multi-Stage Illumination Modeling of Dermatological Photographs for Illumination-Corrected Skin Lesion Analysis", IEEE Transactions on Biomedical Engineering, vol. 60, issue 7, pp. 1873 - 1883, November, 2013.
Benchmarks
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University of Waterloo skin cancer database
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DTP-Net
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University of Waterloo skin cancer database
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DTP-Net
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EMST
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Papers
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