Preparing Medical Imaging Data for Machine Learning
University College London · Stanford University · +1 more institution
Abstract
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…
Citation impact
- FWCI
- 59.90
- Percentile
- 100%
- References
- 101
Authors
10- MJMartin J. WilleminkCorresponding
University College London, Stanford University
- WAWojciech A. Koszek
University College London, Stanford University
- CHCailin Hardell
University College London, Stanford University
- JWJie Wu
University College London, Stanford University
- DFDominik Fleischmann
University College London, Stanford University
Topics & keywords
- Artificial intelligence
- Machine learning
- Process (computing)
- Medical imaging
- Data curation
- Medicine
- Data science
- Generalization