Preparing Medical Imaging Data for Machine Learning
By Martin J. Willemink, W. A. Koszek, Cailin Hardell, Jie Wu, Dominik Fleischmann, H. Harvey, Les R. Folio, Ronald M. Summers, Daniel L. Rubin, Matthew P. Lungren. Radiology, 2020
891 citations recorded by the source corpus.
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.
Subfields: Biomedical Engineering, Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/3893783a815a5d46a26f154683115d4195b1b004
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In Biomedical Engineering
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Citations in the corpus
- AI in health and medicine (2022, methodology): “Another issue affecting images as well as many other types of medical data is a shortage of the labels required for supervised learning 94 .”
- Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. (2020, methodology): “Describe if and how imaging labels were extracted from free-text imaging reports or electronic health records using natural language processing or recurrent neural networks (20,27,28).”
- A foundation model for generalizable disease detection from retinal images (2023, methodology): “These models are typically developed using large volumes of high-quality labels, which requires expert assessment and laborious workload 1,2 .”