Abstract:
Recent advances in machine learning algorithms, as well as their future prospects, provide useful applications in medical imaging. Machine learning has the ability to enhance several aspects of the radiology and nuclear medicine workflow, such as order scheduling and triage, clinical decision support systems, finding detection and interpretation, post-processing and dosage estimate, examination quality control, and radiology reporting. Machine learning has the potential to make medical imaging systems smarter. Intelligent imaging systems could reduce unnecessary imaging, improve positioning, and help improve the characterization of the findings. Despite this, there are still numerous obstacles to overcome before any of these procedures can be automated using machine learning. In this talk, We highlight areas where machine learning has previously been deployed and indicate places where we could spend more resources.
Page(s):
329-329
DOI:
DOI not available
Published:
Journal: Abstract Book on International Conference on Food and Applied Sciences (ICFAS-23) 3-5 August 23, Volume: 0, Issue: 0, Year: 2023
Keywords:
Medical Imaging
,
Machine learning
,
Dose
,
Radiology
,
Nuclear Medicine