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Organized by the University of Chicago’s Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship Program.

Agenda
4:00pm – 4:45pm:  Presentation
4:45pm – 5:00pm:  Q&A
5:00pm – 5:30pm: Reception

Meeting location
William Eckhardt Research Center. Room 401
5640 S Ellis Avenue, Chicago, IL 60637
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Abstract: Medical imaging science is an exciting interdisciplinary field shaped by advancements in physics, chemistry, biology, computer science, and more. Artificial intelligence and data science methods have made important contributions to it for several decades, resulting in a wide array of tools and approaches available for enhancing medical imaging use in clinical medicine. In this talk, I will present our recent work on pre-operative ovarian cancer diagnosis on ultrasound imaging and personalized, patient-specific AI outputs for breast cancer diagnosis on magnetic resonance imaging. These applications demonstrate the value of hybrid AI, which integrates multiple AI approaches in a pipeline, and the importance of evaluating AI performance at the patient level, which can be important for clinician consideration and also yield benefits for pipeline evaluation.

Bio: Heather M. Whitney, PhD is an assistant professor in the Department of Radiology at the University of Chicago. Dr. Whitney received a Master of Science in Medical Physics from the Vanderbilt University School of Medicine and Master of Science and PhD in Physics from Vanderbilt University. While at Vanderbilt, she trained and conducted research at the Vanderbilt University Institute of Imaging Science and additionally collaborated with faculty in the Department of Radiation Oncology.

At the University of Chicago, she conducts research in computer-aided diagnosis of breast and ovarian cancer, focusing on the modalities of dynamic contrast-enhanced magnetic resonance imaging and ultrasound. Her primary areas of interest are in artificial intelligence and radiomics across the imaging and classification pipeline, from image acquisition to performance evaluation and data harmonization to clinical implementation. She also conducts research and collaborates in MIDRC, the Medical Imaging and Data Resource Center. Within MIDRC she works on methods of task-based distributions, interoperability between data enclaves, and monitoring and studying the diversity and representativeness of the MIDRC data commons to foster research in AI and health disparities. She is also a faculty member in the MacLean Center for Clinical Medical Ethics at the University of Chicago, where she conducts research into the ethics of AI in medical imaging.

Parking
Campus North Parking
5505 S Ellis Ave
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