Dr. Geoffrey D. Rubin
at Duke Univ
SPIE Involvement:
Author
Profile Summary

Geoffrey D. Rubin, MD, MBA is the George B. Geller Distinguished Professor for Research in Cardiovascular Diseases and former chair of the Department of Radiology at Duke University. Previously, he was Professor of Radiology at Stanford University where he co-founded the Stanford 3-D Medical Imaging Laboratory in 1996 and served as its Medical Director until 2010, establishing the first scalable clinical service facility for applying computer graphics and vision tools to medical imaging data and training hundreds of physicians and technologists to emulate the model worldwide. His research focuses on applications of machine learning to medical image analysis, human perception in the interpretation of volumetric medical images, and the application of CT and MRI for assessing the cardiovascular and respiratory systems.
He is past president of the North American Society for Cardiovascular Imagers, the Society for Advanced Body Imaging, and the Fleischner Society. He has authored over 220 peer-reviewed manuscripts and edited six books, including the acclaimed textbook, CT and MR Angiography: Comprehensive Vascular Assessment.
Publications (16)

SPIE Journal Paper | 25 January 2020
JMI Vol. 7 Issue 02
KEYWORDS: Lung, Computed tomography, Performance modeling, Statistical analysis, Statistical modeling, Blood vessels, Chest, Lung cancer, Data modeling, Feature selection

Proceedings Article | 15 March 2019
Proc. SPIE. 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
KEYWORDS: Image segmentation, Data modeling, 3D modeling, Computed tomography, Kidney, 3D image processing, Medical imaging, Machine learning

Proceedings Article | 13 March 2019
Proc. SPIE. 10950, Medical Imaging 2019: Computer-Aided Diagnosis
KEYWORDS: Computed tomography, Kidney, Lung, Performance modeling, Machine learning, Statistical modeling, Medicine

Proceedings Article | 13 March 2019
Proc. SPIE. 10950, Medical Imaging 2019: Computer-Aided Diagnosis
KEYWORDS: Lung, Chest, Radiology, Image classification, Convolution, Neural networks, Computer aided diagnosis and therapy, Machine learning

Proceedings Article | 13 March 2019
Proc. SPIE. 10950, Medical Imaging 2019: Computer-Aided Diagnosis
KEYWORDS: Computed tomography, Lung, Radiology, Machine learning, Rule based systems, Kidney, Liver, Medical imaging, Performance modeling, Data modeling

Showing 5 of 16 publications
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