Join the Machine Learning in Medical Imaging Consortium (MaLMIC) for an opportunity to network
Natural Language Processing
Friday, October 28, 2022
3:00 to 4:30 p.m. ET
Richard Do, a radiologist, will start the forum with a presentation on natural language processing (NLP) of radiology reports to track metastatic spread. Heidi Hanson, a researcher, will talk about NLP in cancer surveillance. The two presentations will be followed by discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.
Interested in joining? Please contact us.
Natural Language Processing
Friday, October 28, 2022
3:00 to 4:30 p.m. ET
Richard Do, a radiologist, will start the forum with a presentation on natural language processing (NLP) of radiology reports to track metastatic spread. Heidi Hanson, a researcher, will talk about NLP in cancer surveillance. The two presentations will be followed by discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.
Interested in joining? Please contact us.
Insights from Natural Language Processing Applied to Oncologic Radiology Reports
Richard Kinh Gian Do, MD, PhD
Radiologist, Memorial Sloan Kettering Cancer Center
Talk Summary: Radiology reports routinely describe physiologic and pathologic processes, including sites of disease and treatment response in cancer patients. At Memorial Sloan Kettering Cancer Center, over a half million radiology reports are created annually. The development of NLP models is an essential step in helping investigators accurately extract large-scale structured data from such reports. Clinical insights from the initial efforts to catalog the spread of metastatic disease will be discussed.
Real-time Cancer Reporting at Scale: How NCI and DOE are forging a new path for population-level cancer surveillance
Heidi Hanson, PhD
Group Leader, Biostatistics and Multiscale System Modeling, Oak Ridge National Laboratory
Talk summary: Heidi will discuss the NCI-DOE Modeling Outcomes using Surveillance data and Scalable AI for Cancer (MOSSAIC) project. She will give an overview of their approach to translational AI for cancer surveillance and their attempt to modernize national cancer surveillance with deep learning solutions.