T32 Presentations: Drs. John Colby and Andreas Rauschecker
Date
Type
Time Duration
Location
Notes
Broadcasts to:
China Basin, Large Classroom
Mt. Zion, 1600 Divisadero Street, C250
Mission Bay Hospital, 1975 4th Street, C1719
VAMC Bldg 200 Room 2A-147
ZSFG, Radiology, Room: Minagi Library
Connect via Zoom: https://zoom.us/j/9055865224
Speakers
![](https://radiology.ucsf.edu/sites/radiology.ucsf.edu/files/styles/150w/public/fields/field_image/speakers/Image%20for%202019%20T32%20lectures_4x4.jpg?itok=0_Puuxq4)
The Department of Radiology and Biomedical Imaging has a long record of excellence in clinical and academic radiology, and has one of the largest research enterprises funded through intra- and extramural funding and private donors. With numerous outstanding basic scientists and clinicians engaged in innovative imaging research across five principal campus units, the department provides a fertile ground for interdisciplinary collaboration. The T32 program exists to jumpstart the academic careers of junior radiologists and nuclear medicine physicians and to provide the essential foundation for developing a research program as an independent investigator.
![](https://radiology.ucsf.edu/sites/radiology.ucsf.edu/files/styles/150w/public/fields/field_image/speakers/John_Colby_IMAGES.jpg?itok=L3D41ruj)
Lecture Title: 'How to train your dragon: A Recipe for End-to-End Automated Imaging Quantification at UCSF'
John B. Colby, MD, PhD received his medical degree and PhD from UC Los Angeles. His diagnostic radiology residency at UCSF will be completed in June 2019. He served as a T32 fellow in 2018-2019. Dr. Colby’s research focus is on algorithms development for medical imaging analysis. Dr. Colby will continue his training at UCSF as a Neuroradiology fellow in July 2019.
![](https://radiology.ucsf.edu/sites/radiology.ucsf.edu/files/styles/150w/public/fields/field_image/speakers/Andreas_Rauschecker.jpg?itok=X708U71v)
Lecture Title: 'Artificial Intelligence System for Neuroradiologist-Level Differential Diagnosis on Brain MRI'
Andreas Rauschecker is a clinical fellow in neuroradiology in the UCSF Department of Radiology and Biomedical Imaging. As a 2018-2019 NIH T-32 scholar, Andreas' research investigations over the past year have focused on developing and testing a system for using artificial intelligence on clinical brain MRIs. The system uses deep learning and image processing technologies to extract features of interest to radiologists and clinicians, and then it combines these imaging features with clinical features to develop differential diagnoses.