How to Translate ML into the Clinic
Shreyas Vasanawala1
1Stanford University, Stanford, CA, United States
Synopsis
Machine learning (ML) has the potential to impact strongly medical imaging. Though much attention has been focused on image analysis, ML is poised to improve imaging at all steps of the medical imaging chain. This presentation will provide an overview of the significant barriers to widespread translation of ML, the steps in the medical imaging chain at which ML can be applied, and examples of approaches that have enabled use in clinical settings.
Overview
Machine learning (ML) has the potential to impact strongly medical imaging. Though much attention has been focused on image analysis, ML is poised to improve imaging at all steps of the medical imaging chain. This presentation will provide an overview of the significant barriers to widespread translation of ML, the steps in the medical imaging chain at which ML can be applied, and examples of approaches that have enabled use in clinical settings.Acknowledgements
No acknowledgement found.References
No reference found.
Proc. Intl. Soc. Mag. Reson. Med. 29 (2021)