Preparing Medical Imaging Data for Machine Learning
Open accessAbstract
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.
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Discussion of proper data handling in existing guidelines for machine learning system development is often insufficient.
“Although several guidelines exist for the proper development of ML systems, discussion of proper data handling is often insufficient (8,9).”
From Mitigating Bias in Radiology Machine Learning: 1. Data Handling (2022) · cites this paper as [8]
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References 98 · Cited by 1,020
- Authors
- Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P.
- Affiliations
- Stanford Medicine (US)
- National Institutes of Health (US)
- ORCID
- Venue
- Radiology
- Published
- 2020-04
- Volume
- 295
- Issue
- 1
- Pages
- 4-15
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC7104701
- OpenAlex
- W3007935259
- Cited by
- 1,020
- References
- 98
- Citations by year
- 2026: 154 · 2025: 176 · 2024: 194 · 2023: 177 · 2022: 174 · 2021: 121 · 2020: 21 · 2018: 2
- FWCI
- 36.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Center for Advancing Translational Sciences (UL1 TR001085)
- National Cancer Institute (U01CA142555)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- data curation, training data availability, expert annotation, supervised learning, data generalization, artificial intelligence in medical imaging
- MeSH terms
- Machine Learning, Data Management, Algorithms, Data Collection, Diagnostic Imaging, Humans
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