# BCN20000: Dermoscopic Lesions in the Wild

arXiv: https://arxiv.org/abs/1908.02288 · HTML: https://mlsanity.com/paper/1908.02288/

## Key facts

- Cited by 225 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 30 of 225)

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- [Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification](https://mlsanity.com/paper/2102.01284/) (2021)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
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- [ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions](https://mlsanity.com/paper/2201.01249/) (2022)
- [FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification](https://mlsanity.com/paper/2303.08325/) (2023)
- [FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification](https://mlsanity.com/paper/2206.13803/) (2022)
- [Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma](https://mlsanity.com/paper/2303.12806/) (2023)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions](https://mlsanity.com/paper/2208.13528/) (2022)
- [PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion](https://mlsanity.com/paper/2503.00196/) (2025)
- [Toward Fairness Through Fair Multi-Exit Framework for Dermatological Disease Diagnosis](https://mlsanity.com/paper/2306.14518/) (2023)
- [A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis](https://mlsanity.com/paper/2405.14839/) (2024)
- [MM-Skin: Enhancing Dermatology Vision-Language Model with an Image-Text Dataset Derived from Textbooks](https://mlsanity.com/paper/2505.06152/) (2025)
- [Pretrained ViTs Yield Versatile Representations For Medical Images](https://mlsanity.com/paper/2303.07034/) (2023)

## Abstract (arXiv)

This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Cl'inic in Barcelona. With this dataset, we aim to study the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions found in hard-to-diagnose locations (nails and mucosa), large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. The BCN20000 will be provided to the participants of the ISIC Challenge 2019, where they will be asked to train algorithms to classify dermoscopic images of skin cancer automatically.