Eliot L. Siegel
University of Maryland, Baltimore
182 Papers
1.4K Citations
Eliot L. Siegel is an academic researcher from University of Maryland, Baltimore. The author has contributed to research in topics: Medicine & Picture archiving and communication system. The author has an hindex of 37, co-authored 169 publications. Previous affiliations of Eliot L. Siegel include Carestream Health & Veterans Health Administration.
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Papers
•Posted Content
Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
Ophir Gozes,Maayan Frid-Adar,Hayit Greenspan,Patrick D. Browning,Huangqi Zhang,Wenbin Ji,Adam Bernheim,Eliot L. Siegel +7 more
TL;DR: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus demonstrate they can differentiate coronavirus patients from non-patients and measure the progression of disease in each patient over time using a 3D volume review.
Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment
TL;DR: The combination of AI, big data, and massively parallel computing offers the potential to create a revolutionary way of practicing evidence-based, personalized medicine.
492
Effects of Age, Gender, and Myostatin Genotype on the Hypertrophic Response to Heavy Resistance Strength Training
Frederick M. Ivey,Stephen M. Roth,Robert E. Ferrell,Brian L. Tracy,Jeffrey T. Lemmer,D. E. Hurlbut,G. F. Martel,Eliot L. Siegel,James L. Fozard,E. Jeffrey Metter,Jerome L. Fleg,Ben F. Hurley +11 more
TL;DR: Age does not affect the muscle mass response to either ST or detraining, whereas gender does, as men increased their muscle volume about twice as much in response to ST as did women and experienced larger losses in responseto detraining than women.
303
Fast and effective retrieval of medical tumor shapes
TL;DR: A natural similarity function for shape matching is used, based on concepts from mathematical morphology, and it is shown how it can be lower-bounded by a set of shape features for safely pruning candidates, thus giving fast and correct output.
216
Implementing Virtual and Augmented Reality Tools for Radiology Education and Training, Communication, and Clinical Care.
Raul N. Uppot,Benjamin Laguna,Colin J. McCarthy,Gianluca De Novi,Andrew Phelps,Eliot L. Siegel,Jesse Courtier +6 more
TL;DR: The purpose of this review article is to summarize how three institutions have explored using virtual and augmented reality for radiology.
174