{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T22:20:58Z","timestamp":1761171658525,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:00:00Z","timestamp":1760745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2022 Ph.D. Training Program for Research Staff of the University of Las Palmas de Gran Canaria"},{"DOI":"10.13039\/501100000780","name":"European Union\u2019s Horizon 2020 research and innovation programme","doi-asserted-by":"publisher","award":["101017385"],"award-info":[{"award-number":["101017385"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005416","name":"IKT+ initiative","doi-asserted-by":"publisher","award":["332901"],"award-info":[{"award-number":["332901"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Well-annotated datasets are fundamental for developing robust artificial intelligence models, particularly in medical fields. Many existing skin lesion datasets have limitations in image diversity (including only clinical or dermoscopic images) or metadata, which hinder their utility for mimicking real-world clinical practice. The purpose of the MCR-SL dataset is to introduce a new, meticulously curated dataset that addresses these limitations. The MCR-SL dataset was collected from 60 subjects at the University Hospital of North Norway and comprises 779 clinical images and 1352 dermoscopic images of 240 unique lesions. The lesion types included are nevus, seborrheic keratosis, basal cell carcinoma, actinic keratosis, atypical nevus, melanoma, squamous cell carcinoma, angioma, and dermatofibroma. Labels were established by combining the consensus of a panel of four dermatologists with histopathology reports for the 29 excised lesions, with the latter serving as the gold standard. The resulting dataset provides a comprehensive resource with clinical and dermoscopic images and rich clinical context, ensuring a high level of clinical relevance, surpassing many existing resources in that matter. The MCR-SL dataset provides a holistic and reliable foundation for validating artificial intelligence models, enabling a more nuanced and clinically relevant approach to automated skin lesion diagnosis that mirrors real-world clinical practice.<\/jats:p>","DOI":"10.3390\/data10100166","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T09:23:34Z","timestamp":1760952214000},"page":"166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MCR-SL: A Multimodal, Context-Rich Skin Lesion Dataset for Skin Cancer Diagnosis"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9538-4569","authenticated-orcid":false,"given":"Maria","family":"Castro-Fernandez","sequence":"first","affiliation":[{"name":"Research Institute for Applied Microelectronics (IUMA), Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas Roger","family":"Schopf","sequence":"additional","affiliation":[{"name":"Norwegian Center for E-Health Research, University Hospital of North-Norway, 9038 Troms\u00f8, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8188-7066","authenticated-orcid":false,"given":"Irene","family":"Casta\u00f1o-Gonzalez","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Hospital Universitario de Gran Canaria Dr. Negr\u00edn, Barranco de la Ballena s\/n, 35010 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1650-6407","authenticated-orcid":false,"given":"Belinda","family":"Roque-Quintana","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Hospital Universitario de Gran Canaria Dr. Negr\u00edn, Barranco de la Ballena s\/n, 35010 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Herbert","family":"Kirchesch","sequence":"additional","affiliation":[{"name":"Dermatology Private Office, 51147 Cologne, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7519-954X","authenticated-orcid":false,"given":"Samuel","family":"Ortega","sequence":"additional","affiliation":[{"name":"Research Institute for Applied Microelectronics (IUMA), Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain"},{"name":"Norwegian Institute of Food, Fisheries and Aquaculture Research (Nofima), 9291 Troms\u00f8, Norway"},{"name":"Department of Mathematics and Statistics, UiT The Arctic University of Norway, 9037 Troms\u00f8, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9794-490X","authenticated-orcid":false,"given":"Himar","family":"Fabelo","sequence":"additional","affiliation":[{"name":"Research Institute for Applied Microelectronics (IUMA), Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain"},{"name":"Fundaci\u00f3n Canaria Instituto de Investigaci\u00f3n Sanitaria de Canarias (FIISC), 35019 Las Palmas de Gran Canaria, Spain"},{"name":"Research Unit, Hospital Universitario de Gran Canaria Dr. Negr\u00edn, 35019 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7896-8634","authenticated-orcid":false,"given":"Fred","family":"Godtliebsen","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, UiT The Arctic University of Norway, 9037 Troms\u00f8, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Concei\u00e7\u00e3o","family":"Granja","sequence":"additional","affiliation":[{"name":"Norwegian Center for E-Health Research, University Hospital of North-Norway, 9038 Troms\u00f8, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3784-5504","authenticated-orcid":false,"given":"Gustavo M.","family":"Callico","sequence":"additional","affiliation":[{"name":"Research Institute for Applied Microelectronics (IUMA), Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1001\/jamadermatol.2025.1276","article-title":"Burden of Skin Cancer in Older Adults from 1990 to 2021 and Modelled Projection to 2050","volume":"161","author":"Wang","year":"2025","journal-title":"JAMA Dermatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ejca.2019.04.001","article-title":"Deep Learning Outperformed 136 of 157 Dermatologists in a Head-to-Head Dermoscopic Melanoma Image Classification Task","volume":"113","author":"Brinker","year":"2019","journal-title":"Eur. J. Cancer"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1836","DOI":"10.1093\/annonc\/mdy166","article-title":"Man against Machine: Diagnostic Performance of a Deep Learning Convolutional Neural Network for Dermoscopic Melanoma Recognition in Comparison to 58 Dermatologists","volume":"29","author":"Haenssle","year":"2018","journal-title":"Ann. Oncol."},{"key":"ref_5","unstructured":"Ha, Q., Liu, B., and Liu, F. (2020). Identifying Melanoma Images Using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2497","DOI":"10.1007\/s00432-021-03809-x","article-title":"Non-Melanoma Skin Cancer Diagnosis: A Comparison between Dermoscopic and Smartphone Images by Unified Visual and Sonification Deep Learning Algorithms","volume":"148","author":"Dascalu","year":"2021","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pacheco, A.G.C., and Krohling, R.A. (2020). The Impact of Patient Clinical Information on Automated Skin Cancer Detection. Comput. Biol. Med., 116.","DOI":"10.1016\/j.compbiomed.2019.103545"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3554","DOI":"10.1109\/JBHI.2021.3062002","article-title":"An Attention-Based Mechanism to Combine Images and Metadata in Deep Learning Models Applied to Skin Cancer Classification","volume":"25","author":"Pacheco","year":"2021","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Castro-Fernandez, M., Hernandez, A., Fabelo, H., Balea-Fernandez, F.J., Ortega, S., and Callico, G.M. (September, January 31). Towards Skin Cancer Self-Monitoring through an Optimized MobileNet with Coordinate Attention. Proceedings of the 2022 25th Euromicro Conference on Digital System Design (DSD), Maspalomas, Spain.","DOI":"10.1109\/DSD57027.2022.00087"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nie, Y., Sommella, P., Carrat\u00f9, M., O\u2019Nils, M., and Lundgren, J. (2022). A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss. Diagnostics, 13.","DOI":"10.3390\/diagnostics13010072"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109544","DOI":"10.1109\/ACCESS.2024.3439365","article-title":"A Large Dataset to Enhance Skin Cancer Classification with Transformer-Based Deep Neural Networks","volume":"12","author":"Gallazzi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"180161","DOI":"10.1038\/sdata.2018.161","article-title":"The HAM10000 Dataset, a Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions","volume":"5","author":"Tschandl","year":"2018","journal-title":"Sci. Data"},{"key":"ref_13","unstructured":"Combalia, M., Codella, N.C.F., Rotemberg, V., Helba, B., Vilaplana, V., Reiter, O., Carrera, C., Barreiro, A., Halpern, A.C., and Puig, S. (2019). BCN20000: Dermoscopic Lesions in the Wild. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mendonca, T., Ferreira, P.M., Marques, J.S., Marcal, A.R.S., and Rozeira, J. (2013, January 3\u20137). PH2\u2014A Dermoscopic Image Database for Research and Benchmarking. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106221","DOI":"10.1016\/j.dib.2020.106221","article-title":"PAD-UFES-20: A Skin Lesion Dataset Composed of Patient Data and Clinical Images Collected from Smartphones","volume":"32","author":"Pacheco","year":"2020","journal-title":"Data Brief"},{"key":"ref_16","unstructured":"Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., and Marchetti, M. (2019). Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC). arXiv."},{"key":"ref_17","unstructured":"(2021, October 27). Watching the Risk Factors: Artificial Intelligence and the Prevention of Chronic Conditions|WARIFA Project|Fact Sheet|H2020|CORDIS|European Commission. Available online: https:\/\/cordis.europa.eu\/project\/id\/101017385\/es."},{"key":"ref_18","first-page":"e19","article-title":"Quantifying Acceptable Artefact Ranges for Dermatologic Classification Algorithms","volume":"1","author":"Petrie","year":"2021","journal-title":"Ski. Health Dis."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1038\/s41591-025-03747-y","article-title":"A Multimodal Vision Foundation Model for Clinical Dermatology","volume":"31","author":"Yan","year":"2025","journal-title":"Nat. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e1465","DOI":"10.1002\/wics.1465","article-title":"Recent Advances in Hyperspectral Imaging for Melanoma Detection","volume":"12","author":"Johansen","year":"2020","journal-title":"WIREs Comput. Stat."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Leon, R., Martinez-Vega, B., Fabelo, H., Ortega, S., Melian, V., Casta\u00f1o, I., Carretero, G., Almeida, P., Garcia, A., and Quevedo, E. (2020). Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support. J. Clin. Med., 9.","DOI":"10.3390\/jcm9061662"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Aloupogianni, E., Ishikawa, M., Ichimura, T., Sasaki, A., Kobayashi, N., and Obi, T. (2021, January 1\u20135). Design of a Hyper-Spectral Imaging System for Gross Pathology of Pigmented Skin Lesions. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Guadalajara, Mexico.","DOI":"10.1109\/EMBC46164.2021.9629512"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hetz, M.J., Garcia, C.N., Haggenm\u00fcller, S., and Brinker, T.J. (2024). Advancing Dermatological Diagnosis: Development of a Hyperspectral Dermatoscope for Enhanced Skin Imaging. arXiv.","DOI":"10.21203\/rs.3.rs-4129124\/v1"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"De Pascalis, A., Perrot, J.L., Tognetti, L., Rubegni, P., and Cinotti, E. (2021). Review of Dermoscopy and Reflectance Confocal Microscopy Features of the Mucosal Melanoma. Diagnostics, 11.","DOI":"10.3390\/diagnostics11010091"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1364\/BOE.559842","article-title":"Four-Modal Device Comprising Optical Coherence Tomography, Photoacoustic Tomography, Ultrasound, and Raman Spectroscopy Developed for in Vivo Skin Lesion Assessment","volume":"16","author":"Roth","year":"2025","journal-title":"Biomed. Opt. Express"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1080\/01676830.2024.2331718","article-title":"Functional and Molecular 3D Mapping of Angiosarcoma Tumor Using Non-Invasive Laser Speckle, Hyperspectral, and Photoacoustic Imaging","volume":"43","author":"Stridh","year":"2024","journal-title":"Orbit"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, D., Fedorov Kukk, A., Panzer, R., Emmert, S., and Roth, B. (2025). In Vivo Differentiation of Cutaneous Melanoma from Benign Nevi with Dual\u2013Modal System of Optical Coherence Tomography and Raman Spectroscopy. J. Biophotonics, 18.","DOI":"10.1002\/jbio.70040"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/s41597-021-00815-z","article-title":"A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context","volume":"8","author":"Rotemberg","year":"2021","journal-title":"Sci. Data"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1001\/jamadermatol.2021.4915","article-title":"Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines from the International Skin Imaging Collaboration Artificial Intelligence Working Group","volume":"158","author":"Daneshjou","year":"2022","journal-title":"JAMA Dermatol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e068207","DOI":"10.1136\/bmjopen-2022-068207","article-title":"Diagnostic Reliability in Teledermatology: A Systematic Review and a Meta-Analysis","volume":"13","author":"Bourkas","year":"2023","journal-title":"BMJ Open"},{"key":"ref_31","unstructured":"(2025, October 02). ISIC Archive. ISIC 2020: Training Data, Available online: https:\/\/gallery.isic-archive.com\/#!\/topWithHeader\/onlyHeaderTop\/gallery?filter=%5B%22collections%7C70%22%5D."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1111\/j.1440-0960.2005.00189.x","article-title":"Assessing Diagnostic Skill in Dermatology: A Comparison between General Practitioners and Dermatologists","volume":"46","author":"Tran","year":"2005","journal-title":"Australas. J. Dermatol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1177\/1357633X241286003","article-title":"Teledermoscopic Triage of Melanoma-Suspicious Skin Lesions Is Safe: A Retrospective Comparative Diagnostic Accuracy Study with Multiple Assessors","volume":"31","author":"Nervil","year":"2025","journal-title":"J. Telemed. Telecare"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1109\/JBHI.2018.2845939","article-title":"A Survey of Feature Extraction in Dermoscopy Image Analysis of Skin Cancer","volume":"23","author":"Barata","year":"2019","journal-title":"IEEE J. Biomed. Health Inf."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/10\/166\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T04:00:24Z","timestamp":1761105624000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/10\/166"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,18]]},"references-count":34,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["data10100166"],"URL":"https:\/\/doi.org\/10.3390\/data10100166","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2025,10,18]]}}}