2026-10-14T10:41:35Zhttps://www.radar-service.eu/oai/OAIHandler10.35097/j3ntzrcmepaygjxv2026-10-09T11:06:39Zradar4kit
https://dx.doi.org/10.35097/j3ntzrcmepaygjxv
Marinov, Zdravko
OmniMedSeg-Dermoscopy
Computer Vision for Human Computer Interactions Lab (cv:hci), Institute for Anthropomatics and Robotics (IAR), Karlsruhe Institute of Technology
2026
Computer Science
dataset
Dataset
info:eu-repo/semantics/openAccess
Other
info:eu-repo/semantics/openAccess
Please read the Description for more details about the dataset licenses and terms of use. FUSC_2021: CC BY-NC HAM_10000: CC-BY-NC 4.0 ISIC_2016: CC0 1.0 ISIC_2017: CC0 1.0 ISIC_2018: CC-BY-NC 4.0 NEVUS: CC-BY 4.0 SKIN_HAIR: CC-BY 4.0
This dataset contains 7 converted Dermoscopy datasets, part of the OmniMedSeg superset. All datasets are converted to a standardized structure with binary masks for each segmentation target.
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DATASET LICENSE AND CITATION SUMMARY
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QUICK REFERENCE: DATASETS AND LICENSES
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FUSC_2021: CC BY-NC
HAM_10000: CC-BY-NC 4.0
ISIC_2016: CC0 1.0
ISIC_2017: CC0 1.0
ISIC_2018: CC-BY-NC 4.0
NEVUS: CC-BY 4.0
SKIN_HAIR: CC-BY 4.0
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DETAILED INFORMATION BY DATASET
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[1] FUSC_2021
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License: CC BY-NC
Dataset link: https://github.com/uwm-bigdata/wound-segmentation/tree/master/data/Foot%20Ulcer%20Segmentation%20Challenge
Metadata file: Dermoscopy/FUSC_2021/metadata.json
Citation (bibtex):
@article{Wang2020,
title = {Fully automatic wound segmentation with deep convolutional neural networks},
volume = {10},
ISSN = {2045-2322},
url = {http://dx.doi.org/10.1038/s41598-020-78799-w},
DOI = {10.1038/s41598-020-78799-w},
number = {1},
journal = {Scientific Reports},
publisher = {Springer Science and Business Media LLC},
author = {Wang, Chuanbo and Anisuzzaman, D. M. and Williamson, Victor and Dhar, Mrinal Kanti and Rostami, Behrouz and Niezgoda, Jeffrey and Gopalakrishnan, Sandeep and Yu, Zeyun},
year = {2020},
month = Dec
}
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[2] HAM_10000
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License: CC-BY-NC 4.0
Dataset link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T
Metadata file: Dermoscopy/HAM_10000/metadata.json
Citation (bibtex):
@article{Tschandl2020,
title = {Human–computer collaboration for skin cancer recognition},
volume = {26},
ISSN = {1546-170X},
url = {http://dx.doi.org/10.1038/s41591-020-0942-0},
DOI = {10.1038/s41591-020-0942-0},
number = {8},
journal = {Nature Medicine},
publisher = {Springer Science and Business Media LLC},
author = {Tschandl, Philipp and Rinner, Christoph and Apalla, Zoe and Argenziano, Giuseppe and Codella, Noel and Halpern, Allan and Janda, Monika and Lallas, Aimilios and Longo, Caterina and Malvehy, Josep and Paoli, John and Puig, Susana and Rosendahl, Cliff and Soyer, H. Peter and Zalaudek, Iris and Kittler, Harald},
year = {2020},
month = June,
pages = {1229–1234}
}
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[3] ISIC_2016
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License: CC0 1.0
Dataset link: https://challenge.isic-archive.com/data/#2016
Metadata file: Dermoscopy/ISIC_2016/metadata.json
Citation (bibtex):
@misc{https://doi.org/10.48550/arxiv.1605.01397,
doi = {10.48550/ARXIV.1605.01397},
url = {https://arxiv.org/abs/1605.01397},
author = {Gutman, David and Codella, Noel C. F. and Celebi, Emre and Helba, Brian and Marchetti, Michael and Mishra, Nabin and Halpern, Allan},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)},
publisher = {arXiv},
year = {2016},
copyright = {arXiv.org perpetual, non-exclusive license}
}
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[4] ISIC_2017
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License: CC0 1.0
Dataset link: https://challenge.isic-archive.com/data/#2017
Metadata file: Dermoscopy/ISIC_2017/metadata.json
Citation (bibtex):
@inproceedings{codella2018skin,
title={Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)},
author={Codella, Noel CF and Gutman, David and Celebi, M Emre and Helba, Brian and Marchetti, Michael A and Dusza, Stephen W and Kalloo, Aadi and Liopyris, Konstantinos and Mishra, Nabin and Kittler, Harald and others},
booktitle={2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018)},
pages={168--172},
year={2018},
organization={IEEE}
}
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[5] ISIC_2018
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License: CC-BY-NC 4.0
Dataset link: https://challenge.isic-archive.com/data/#2018
Metadata file: Dermoscopy/ISIC_2018/metadata.json
Citation (bibtex):
@article{Tschandl2018,
title = {The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions},
volume = {5},
ISSN = {2052-4463},
url = {http://dx.doi.org/10.1038/sdata.2018.161},
DOI = {10.1038/sdata.2018.161},
number = {1},
journal = {Scientific Data},
publisher = {Springer Science and Business Media LLC},
author = {Tschandl, Philipp and Rosendahl, Cliff and Kittler, Harald},
year = {2018},
month = Aug
}
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[6] NEVUS
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License: CC-BY 4.0
Dataset link: https://www.kaggle.com/datasets/metavision/accurate-nevus-shapessegmentation
Metadata file: Dermoscopy/NEVUS/metadata.json
Citation (bibtex):
@dataset{Accurate Nevus Shapes,
author={metavision},
title={Accurate Nevus Shapes/Segmentation},
year={2022},
url={https://www.kaggle.com/datasets/metavision/accurate-nevus-shapessegmentation}
}
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[7] SKIN_HAIR
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License: CC-BY 4.0
Dataset link: https://data.mendeley.com/datasets/j5ywpd2p27/2
Metadata file: Dermoscopy/SKIN_HAIR/metadata.json
Citation (bibtex):
@dataset{hossain2023hairmask,
author = {Hossain, Sk Imran and Roy, Sudipta Singha and De Go{\"e}r De Herve, Jocelyn and Mercer, Robert E. and Mephu Nguifo, Engelbert},
title = {A skin lesion hair mask dataset with fine-grained annotations},
year = {2023},
publisher = {Mendeley Data},
version = {V2},
doi = {10.17632/j5ywpd2p27.2}
}
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IMPORTANT NOTES
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- All datasets listed are publicly available
- Full metadata is stored in each dataset's metadata.json file
- For CC0-licensed datasets, attribution is appreciated but not required
- For other licenses, please review the specific terms before use
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