Fusion of biomedical imaging studies for increased sample size and diversity: a case study of brain MRI

Data collection, curation, and cleaning constitute a crucial phase in Machine Learning (ML) projects. In biomedical ML, it is often desirable to leverage multiple datasets to increase sample size and diversity, but this poses unique challenges, which arise from heterogeneity in study design, data de...

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Bibliographic Details
Published in:Frontiers in radiology 2024, Vol.4, p.1283392-1283392
Main Authors: Aiskovich, Matias, Castro, Eduardo, Reinen, Jenna M, Fadnavis, Shreyas, Mehta, Anushree, Li, Hongyang, Dhurandhar, Amit, Cecchi, Guillermo A, Polosecki, Pablo
Format: Article
Language:English
Subjects:
biomedical machine learning
brain MRI
data preparation
data scarcity
dataset fusion
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Summary:Data collection, curation, and cleaning constitute a crucial phase in Machine Learning (ML) projects. In biomedical ML, it is often desirable to leverage multiple datasets to increase sample size and diversity, but this poses unique challenges, which arise from heterogeneity in study design, data descriptors, file system organization, and metadata. In this study, we present an approach to the integration of multiple brain MRI datasets with a focus on homogenization of their organization and preprocessing for ML. We use our own fusion example (approximately 84,000 images from 54,000 subjects, 12 studies, and 88 individual scanners) to illustrate and discuss the issues faced by study fusion efforts, and we examine key decisions necessary during dataset homogenization, presenting in detail a database structure flexible enough to accommodate multiple observational MRI datasets. We believe our approach can provide a basis for future similarly-minded biomedical ML projects.
ISSN:2673-8740
2673-8740
DOI:10.3389/fradi.2024.1283392