From data to diagnosis: skin cancer image datasets for artificial intelligence

Author:

Wen David12ORCID,Soltan Andrew345,Trucco Emanuele6,Matin Rubeta N17

Affiliation:

1. Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK

2. Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, UK

3. Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK

4. Oxford Cancer and Haematology Centre, Oxford University Hospitals NHS Foundation Trust , Oxford , UK

5. Department of Oncology, University of Oxford , Oxford , UK

6. VAMPIRE Project, Computing, School of Science and Engineering, University of Dundee , Dundee , UK

7. Artificial Intelligence Working Party Group, British Association of Dermatologists , London , UK

Abstract

Abstract Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. This review provides an overview of the datasets used to develop AI algorithms and highlights the importance of dataset transparency for the evaluation of algorithm generalizability across varying populations and settings. Current challenges for curation of clinically valuable datasets are detailed, which include dataset shifts arising from demographic variations and differences in data collection methodologies, along with inconsistencies in labelling. These shifts can lead to differential algorithm performance, compromise of clinical utility, and the propagation of discriminatory biases when developed algorithms are implemented in mismatched populations. Limited representation of rare skin cancers and minoritized groups in existing datasets are highlighted, which can further skew algorithm performance. Strategies to address these challenges are presented, which include improving transparency, representation and interoperability. Federated learning and generative methods, which may improve dataset size and diversity without compromising privacy, are also examined. Lastly, we discuss model-level techniques that may address biases entrained through the use of datasets derived from routine clinical care. As the role of AI in skin cancer diagnosis becomes more prominent, ensuring the robustness of underlying datasets is increasingly important.

Publisher

Oxford University Press (OUP)

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Widening the scope of artificial intelligence applications in dermatology;Clinical and Experimental Dermatology;2024-05-10

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