This event has been cancelled. To be rescheduled Fall 2020.
Abstract: The volume of medical imaging has been rapidly increasing and radiologists are struggling to keep up with the sheer number of images; with a goal to impact the entire value chain of imaging from the time the patient comes for a scan to the final delivery of individualized, quantitative, prognostic and care-defining information, we present research on models for disease progression, and outcomes. With an inter-disciplinary team, we address issues related to (i) natural language processing of reports, automatic protocol selection for patient imaging; (ii) faster, safer, quantitative image acquisition; (iii) tissue and organ segmentation; (iv) detection of pathology and (iv) disease trajectory modeling.
BIDS Data Science Research Seminars feature Berkeley faculty and BIDS collaborators doing visionary research that illustrates the character of data science in this new decade. The series is offered to engage our diverse campus community and to enrich connections, discourse, and discovery among colleagues. All seminars are open to the public, and campus community members are especially encouraged to attend. Arrive half-an-hour early for light refreshments and discussion prior to the formal presentation.
Sharmila Majumdar, PhD, is currently Margaret Hart Surbeck Distinguished Professor in Advanced Imaging and Vice Chair for Research in the Department of Radiology and Biomedical Imaging, with joint appointments in the Departments of Bioengineering and Therapeutic Sciences and Orthopedic Surgery at the University of California, San Francisco. She obtained her PhD degree from Yale University in Engineering and Applied Sciences. After a short stay at Yale as a post-doctoral researcher and Assistant Professor, she joined UCSF as an Assistant Professor in 1989. Her research work on imaging, particularly technology development and translational work in magnetic resonance (MR),
µcomputed tomography, simultaneous positron emission tomography/MR, development of image processing, computer vision and machine and deep learning has been focused in the areas of osteoporosis, osteoarthritis, and lower back pain. Her work over the last three decades has been extensively supported by R01, BRP, P50, P30 and other NIH grant mechanisms and industry. Her more recent focus on artificial intelligence applied to biomedical imaging, funded by the NIH R61/R33 mechanism, focusses on translation of methodologies to the clinic. She was selected as a fellow of the American Institute of Medical and Biological Engineers in 2004 and a fellow of the International Society of Magnetic Resonance in Medicine (ISMRM) in 2008. In 2007, the UCSF Haile T. Debas Academy of Medical Educators awarded her the “Excellence in Direct Teaching and/or Excellence in Mentoring and Advising Award”. She was awarded the ISMRM Gold Medal in 2016.