The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
A patient-centric dataset of images and metadata for identifying melanomas using clinical context
Open accessAbstract
Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.
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Self-citationHuman-computer collaboration for skin cancer recognition
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Self-citationNational electrical manufacturers association
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Self-citationBafounta, M.-L., Beauchet, A., Aegerter, P., & Saiag, P. (2001) · Archives of Dermatology · doi:10.1001/archderm.137.10.1343 · Cited by 505
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Self-citationInstruments and new technologies for the in vivo diagnosis of melanoma
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Self-citationThe “Ugly Duckling” Sign: Agreement Between Observers
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Self-citationThe MPATH-Dx reporting schema for melanocytic proliferations and melanoma
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Cited by
Algorithmic fairness in artificial intelligence for medicine and healthcare
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750
Synthetic data in machine learning for medicine and healthcare
Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00751-8 · Cited by 728
Analysis of the ISIC image datasets: Usage, benchmarks and recommendations
Cassidy, B., Kendrick, C., Brodzicki, A., Jaworek-Korjakowska, J., & Yap, M. H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102305 · Cited by 307
The limits of fair medical imaging AI in real-world generalization
Yang, Y., Zhang, H., Gichoya, J. W., Katabi, D., & Ghassemi, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03113-4 · Cited by 285
Characteristics of publicly available skin cancer image datasets: a systematic review
Wen, D., Khan, S. M., Ji Xu, A., Ibrahim, H., Smith, L., Caballero, J., Zepeda, L., de Blas Perez, C., Denniston, A. K., Liu, X., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00252-1 · Cited by 243
Metadata
- Authors
- Rotemberg, V., Kurtansky, N., Betz-Stablein, B., Caffery, L., Chousakos, E., Codella, N., Combalia, M., Dusza, S., Guitera, P., Gutman, D., Halpern, A., Helba, B., Kittler, H., Kose, K., Langer, S., Lioprys, K., Malvehy, J., Musthaq, S., Nanda, J., Reiter, O., Shih, G., Stratigos, A., Tschandl, P., Weber, J., & Soyer, H. P.
- Venue
- Scientific Data
- Published
- 2021-01-28
- Volume
- 8
- Issue
- 1
- Pages
- 34-34
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W3049146198
- Cited by
- 544
- References
- 21
- Type
- data paper
- Language
- en
- Open access
- gold
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- Rotemberg, V., Kurtansky, N., Betz-Stablein, B., Caffery, L., Chousakos, E., Codella, N., Combalia, M., Dusza, S., Guitera, P., Gutman, D., Halpern, A., Helba, B., Kittler, H., Kose, K., Langer, S., Lioprys, K., Malvehy, J., Musthaq, S., Nanda, J., . . . Soyer, H. P. (2021). A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Scientific Data. https://doi.org/10.1038/s41597-021-00815-z
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
