MCR-SL: A Multimodal, Context-Rich Skin Lesion Dataset for Skin Cancer Diagnosis

Maria Castro-Fernandez (), Thomas Roger Schopf, Irene Castaño-Gonzalez, Belinda Roque-Quintana, Herbert Kirchesch, Samuel Ortega, Himar Fabelo, Fred Godtliebsen, Conceição Granja and Gustavo M. Callico ()
Additional contact information
Maria Castro-Fernandez: Research Institute for Applied Microelectronics (IUMA), Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
Thomas Roger Schopf: Norwegian Center for E-Health Research, University Hospital of North-Norway, 9038 Tromsø, Norway
Irene Castaño-Gonzalez: Department of Dermatology, Hospital Universitario de Gran Canaria Dr. Negrín, Barranco de la Ballena s/n, 35010 Las Palmas de Gran Canaria, Spain
Belinda Roque-Quintana: Department of Dermatology, Hospital Universitario de Gran Canaria Dr. Negrín, Barranco de la Ballena s/n, 35010 Las Palmas de Gran Canaria, Spain
Herbert Kirchesch: Dermatology Private Office, 51147 Cologne, Germany
Samuel Ortega: Research Institute for Applied Microelectronics (IUMA), Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
Himar Fabelo: Research Institute for Applied Microelectronics (IUMA), Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
Fred Godtliebsen: Department of Mathematics and Statistics, UiT The Arctic University of Norway, 9037 Tromsø, Norway
Conceição Granja: Norwegian Center for E-Health Research, University Hospital of North-Norway, 9038 Tromsø, Norway
Gustavo M. Callico: Research Institute for Applied Microelectronics (IUMA), Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain

Data, 2025, vol. 10, issue 10, 1-22

Abstract: 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.

Keywords: skin cancer; multimodal; clinical data; dermoscopy; machine learning (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/10/10/166/pdf (application/pdf)
https://www.mdpi.com/2306-5729/10/10/166/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:10:y:2025:i:10:p:166-:d:1774652

Access Statistics for this article

Data is currently edited by Ms. Becky Zhang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().