{"url":"/dataset/university-of-waterloo-skin-cancer-database","name":"University of Waterloo skin cancer database","full_name":null,"description_markdown":"The dataset is maintained by VISION AND IMAGE PROCESSING LAB, University of Waterloo.\r\nThe images of the dataset were extracted from the public databases DermIS and DermQuest, along with manual segmentations of the lesions.\r\n\r\nThe dataset was used in the following journal publication.\r\n[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\r\n[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.\r\n[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.","description_withheld":null,"homepage":"https://uwaterloo.ca/vision-image-processing-lab/research-demos/skin-cancer-detection","introduced_date":"2022-07-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/dtp-net-a-convolutional-neural-network-model","title":"DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images","first_author":"Vipin Venugopal","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Lesion Segmentation","url":"/task/lesion-segmentation","datasets_with_task":"/datasets/task/lesion-segmentation"},{"name":"Skin Lesion Segmentation","url":"/task/skin-lesion-segmentation","datasets_with_task":"/datasets/task/skin-lesion-segmentation"},{"name":"Local Color Enhancement","url":"/task/local-color-enhancement","datasets_with_task":"/datasets/task/local-color-enhancement"}],"languages":[],"variants":["University of Waterloo skin cancer database"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/lesion-segmentation-on-university-of-waterloo","task":"Lesion Segmentation","dataset_variant":"University of Waterloo skin cancer database","rows":5,"metrics":["Dice score"],"first_row_in_archive_order":{"model":"DTP-Net","paper":"/paper/dtp-net-a-convolutional-neural-network-model","metrics":{"Dice score":"0.884 ±0.100"},"code_links":[{"title":"VipinBioLab/DTP-Net","url":"https://github.com/VipinBioLab/DTP-Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/skin-lesion-segmentation-on-university-of","task":"Skin Lesion Segmentation","dataset_variant":"University of Waterloo skin cancer database","rows":5,"metrics":["Dice Score"],"first_row_in_archive_order":{"model":"DTP-Net","paper":"/paper/dtp-net-a-convolutional-neural-network-model","metrics":{"Dice Score":"0.884 ±0.100"},"code_links":[{"title":"VipinBioLab/DTP-Net","url":"https://github.com/VipinBioLab/DTP-Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/local-color-enhancement-on-university-of","task":"Local Color Enhancement","dataset_variant":"University of Waterloo skin cancer database","rows":3,"metrics":["Dice (Average)"],"first_row_in_archive_order":{"model":"EMST","paper":"/paper/an-efficientnet-based-modified-sigmoid","metrics":{"Dice (Average)":"0.81 ± 0.17"},"code_links":[{"title":"VipinBioLab/EMST","url":"https://github.com/VipinBioLab/EMST"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/an-efficientnet-based-modified-sigmoid","title":"An EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesions","date":"2022-07-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dtp-net-a-convolutional-neural-network-model","title":"DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images","date":"2022-07-12","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/histogram-equalization-of-the-image","title":"Histogram Equalization Of The Image","date":"2021-08-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/contrast-limited-adaptive-histogram","title":"Contrast Limited Adaptive Histogram Equalization (CLAHE) Approach for Enhancement of the Microstructures of Friction Stir Welded Joints","date":"2021-08-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/skin-lesion-segmentation-using-atrous","title":"Skin Lesion Segmentation Using Atrous Convolution via DeepLab v3","date":"2018-07-24","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/segnet-a-deep-convolutional-encoder-decoder","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","date":"2015-11-02","rows_on_this_dataset":1,"code_links":74,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":44,"samples_ran":9,"samples_unverified":35,"pointer_only_for_licence":10,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","rows_on_this_dataset":1,"code_links":487,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":757,"samples_ran":510,"samples_unverified":247,"pointer_only_for_licence":426,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fully-connected-deep-structured-networks","title":"Fully Connected Deep Structured Networks","date":"2015-03-09","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":801,"samples_ran":519,"samples_unverified":282,"pointer_only_for_licence":436,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}