Presentations
MO-A-Diagnostic Room-0 (Monday, 4/19/2021) 10:00 AM - 12:00 PM [Eastern Time (GMT-4)]
Adam Yala
Machine Learning for Healthcare
In the first half of the talk, we will discuss how to leverage machine learning to automate tasks humans can do, including the structuring of clinical outcomes from pathology reports and the assessment of breast density. We will use this as an opportunity to introduce the fundamental concepts of supervised learning and to introduce both classical methods such as logistic regression and deep learning. Next, we will discuss how to leverage machine learning to tackle tasks that humans cannot easily do, such as risk assessment and designing dynamic screening policies. In doing so, we will discuss both advanced algorithmic techniques such adversarial training and multi-objective reinforcement learning, and well as the general challenges in developing such systems.
Constance Lehman, MD
AI in Mammography: A Physician’s Perspective
The tools of AI offer promise to tackle some of our greatest challenges in healthcare overall and in mammography specifically. While the initial hype that AI systems will eliminate the role of radiologists in healthcare has waned, questions remain how these tools will be incorporated into “real world” clinical practice. Careful validation studies are needed to determine the true benefit of these tools to avoid a repeat of the history of conventional CAD. As the field evolves to incorporate this new technology into clinical practice, the day-to-day working lives of radiologists will change. This presentation will highlight specific AI tools available for clinical implementation and provide a roadmap for the future of AI in the clinical setting. Specific mammography applications and published research in the specific domains of density assessment, image interpretation, case triage and risk assessment will be reviewed.
Keywords
Mammography, Image Processing, Modeling
Taxonomy
IM- Breast X-Ray Imaging: Machine learning, computer vision
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