A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
08 09 2023
08 09 2023
Historique:
received:
28
11
2022
accepted:
31
07
2023
medline:
11
9
2023
pubmed:
9
9
2023
entrez:
8
9
2023
Statut:
epublish
Résumé
The increasing rates of breast cancer, particularly in emerging economies, have led to interest in scalable deep learning-based solutions that improve the accuracy and cost-effectiveness of mammographic screening. However, such tools require large volumes of high-quality training data, which can be challenging to obtain. This paper combines the experience of an AI startup with an analysis of the FAIR principles of the eight available datasets. It demonstrates that the datasets vary considerably, particularly in their interoperability, as each dataset is skewed towards a particular clinical use-case. Additionally, the mix of digital captures and scanned film compounds the problem of variability, along with differences in licensing terms, ease of access, labelling reliability, and file formats. Improving interoperability through adherence to standards such as the BIRADS criteria for labelling and annotation, and a consistent file format, could markedly improve access and use of larger amounts of standardized data. This, in turn, could be increased further by GAN-based synthetic data generation, paving the way towards better health outcomes for breast cancer.
Identifiants
pubmed: 37684306
doi: 10.1038/s41597-023-02430-6
pii: 10.1038/s41597-023-02430-6
pmc: PMC10491669
doi:
Types de publication
Review
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
595Informations de copyright
© 2023. Springer Nature Limited.
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