ArticleScientific data2024
BCN20000: Dermoscopic Lesions in the Wild.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
123 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review.BMC medical informatics and decision making · 2025Pooled it
- New Trends in Melanoma Detection Using Neural Networks: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.Journal of medical Internet research · 2021Pooled it
- A Reproducible and Explainable Deep Learning Framework for Dermoscopic Image Analysis in Skin Cancer.Journal of clinical medicine · 2026Article
- [Synergistic learnable frequency and multi-scale spatial network for lightweight skin cancer classification].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Dermoscopy-Based AI Risk Scoring Enhanced Experienced Dermatologists' Decision-Making: a Large Retrospective Reader Study.Dermatology practical & conceptual · 2026Article
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- Hybrid Vision Transformer-CNN Architecture with Optimized Feature Selection for Skin Cancer Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Test-fairness deep learning with influence score.PLOS digital health · 2026Article
- Attention guided fair artificial intelligence modeling for skin cancer diagnosis.NPJ digital medicine · 2026Article
- MEL-IA: An Interoperable AI System for Multimodal Skin Lesion Classification in Hospital Settings.Journal of medical systems · 2026Article
- Concept inconsistency in dermoscopic concept bottleneck models: a rough-set analysis of the Derm7pt dataset.Scientific reports · 2026Article
- Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach.NPJ digital medicine · 2026Article
- When AI and Experts Agree on Error: Intrinsic Ambiguity in Dermatoscopic Images.Journal of imaging · 2026Article
- TailBoost: Tail-Synthetic Learning for Boosting Long-Tailed Skin Cancer Image Classification.Sensors (Basel, Switzerland) · 2026Article
- PRISM: a clinically interpretable stepwise framework for multimodal skin cancer diagnosis.Scientific reports · 2026Article
- MultiTask learning AI system to assist BCC diagnosis with dual explanation.Scientific reports · 2026Article
- Towards generalizable AI in medicine via Generalist-Specialist Collaboration.Nature biomedical engineering · 2026Article
- An Optimal Deep Hybrid Framework with Selective Kernel U-Net for Skin Lesion Detection and Classification.Bioengineering (Basel, Switzerland) · 2026Article
63 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
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 Clínic 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.