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106

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Grant support

We acknowledge the support of the International Skin Imaging Collaboration (ISIC). This research was supported by the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033 and the project 718/C/2019 with id 201923-30 and 201923-31, funded by Fundacio la Marato de TV3, iTOBOs grant from the European Union's Horizon 2020 research and innovation programme num 965221. Other funding sources include the Melanoma Research Alliance Young Investigator Award 614197. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748.

Analysis of institutional authors

Combalia, MarcAuthorPodlipnik, SebastianAuthorCarrera, CristinaAuthorBarreiro, AliciaAuthorPuig, SusanaAuthorMalvehy, JosepAuthor

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Article

BCN20000: Dermoscopic Lesions in the Wild

Publicated to:Scientific Data. 11 (1): 641- - 2024-06-17 11(1), DOI: 10.1038/s41597-024-03387-w

Authors: Hernandez-Perez, Carlos; Combalia, Marc; Podlipnik, Sebastian; Codella, Noel C F; Rotemberg, Veronica; Halpern, Allan C; Reiter, Ofer; Carrera, Cristina; Barreiro, Alicia; Helba, Brian; Puig, Susana; Vilaplana, Veronica; Malvehy, Josep

Affiliations

Kitware, Clifton Pk, NY USA - Author
Mem Sloan Kettering Canc Ctr, Dept Med, Dermatol Serv, New York, NY 10021 USA - Author
T Watson Res Ctr, IBM Res AI, Yorktown Hts, NY USA - Author
Univ Barcelona, Hosp Clin Barcelona, Dermatol Dept, Melanoma Unit, Barcelona, Spain - Author
Univ Politecn Cataluna, Signal Theory & Commun, Barcelona, Spain - Author
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Abstract

Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Cl & iacute;nic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation.

Keywords

AccuracAlgorithmsArtificial intelligenceArtificial neural networkClassificationDermatoscopyDermoscopyDiagnosisDiagnostic imagingHumanHumansMachine learningNeural networks, computerSkin neoplasmsSkin tumorSkin-cancerSpain

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Scientific Data due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 16/134, thus managing to position itself as a Q1 (Primer Cuartil), in the category Multidisciplinary Sciences.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-06-26:

  • WoS: 10
  • Scopus: 22
  • Europe PMC: 74

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-26:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 271.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 269 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United States of America.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (Malvehy Guilera, Josep).