January 31, 2018: Dr. Junjun Zhang, Ontario Institute for Cancer Research (OICR)
February 14, 2018: Dr. Gurvaneet Randhawa, NCI, and Dr. George Hripcsak, Columbia University
February 28, 2018: Dr. George Komatsoulis, American Society of Clinical Oncology (ASCO)
TBD: Allen Dearry, NCI
Pathology reports are a primary source of information for cancer registries, which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this talk we will present an update on the NCI-DOE pilot for cancer surveillance, discussing deep learning technology developed and highlighting both theoretical and practical perspectives that are relevant to natural language processing of clinical reports. Using different deep learning architectures, we will present benchmark studies for various information extraction tasks and discuss their importance in supporting a comprehensive and scalable national cancer surveillance program.
• Learn about the motivation and methodology of AI technologies in Radiology
• Learn about the existing and future potential role of radiologic AI with other –omics data for precision medicine.
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