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The Integration of AI and Machine Learning in Medical Physics Applications

V Kearney1*, M Chan2*, C Cardenas3*, (1) University of California San Francisco, San Francsico, CA, (2) Memorial Sloan Kettering Cancer Center, Basking Ridge, NJ, (3) University of Texas MD Anderson Cancer Center, Houston, TX


(Tuesday, 7/16/2019) 11:00 AM - 12:15 PM

Room: Stars at Night Ballroom 4

Abstract: The use of machine learning (ML) and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disciplines. Within the greater diagnostic radiology, radiation oncology, and medical physics communities, promising work is being performed in tissue classification and cancer staging, decision support and outcome prediction, automated segmentation, treatment planning and quality assurance, and numerous other areas. To continue meaningful gains, it is critical to understand that a successful implementation depends as much on the nature of the task as on the nature of the algorithm and the availability and quality of data. In this session, limitations and appropriate use of various ML approaches are explored, highlighting specific applications in image segmentation, treatment planning, and machine and patient QA

Learning objectives:
1. Identify strengths and limitations of machine learning models in medical physics, radiology and radiation oncology
2. Learn how machine learning is being implemented in image segmentation and treatment planning
3. Understand how machine learning can be utilized in patient and machine quality assurance



Statistical Analysis


TH- Dataset analysis/biomathematics: Machine learning techniques

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