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Hamid Jafarkhani

Showing results (1-10 of 9) with videos related to

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IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|January 15, 2005
Power optimization of wireless media systems with space-time block codesHomayoun Yousefi'zadeh, Hamid Jafarkhani, Mehran Moshfeghi
Medical Image Analysis|February 27, 2016
A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRIM R Avendi, Arash Kheradvar, Hamid Jafarkhani
Magnetic Resonance in Medicine|February 17, 2017
Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approachMichael R Avendi, Arash Kheradvar, Hamid Jafarkhani
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|April 2, 2014
Full-reference quality estimation for images with different spatial resolutionsAli Murat Demirtas, Amy R Reibman, Hamid Jafarkhani
IEEE Transactions on Bio-Medical Engineering|April 6, 2016
A 3-D Active Contour Method for Automated Segmentation of the Left Ventricle From Magnetic Resonance ImagesMahdi Hajiaghayi, Elliott M Groves, Hamid Jafarkhani, et al.
Future Cardiology|September 16, 2020
Prospect of artificial intelligence for the assessment of cardiac function and treatment of cardiovascular diseaseArash Kheradvar, Hamid Jafarkhani, Thomas Sloane Guy, et al.
Journal of Cardiovascular Magnetic Resonance : Official Journal of the Society for Cardiovascular Magnetic Resonance|December 1, 2020
Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseasesSaeed Karimi-Bidhendi, Arghavan Arafati, Andrew L Cheng, et al.
Cardiovascular Diagnosis and Therapy|November 19, 2019
Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical needArghavan Arafati, Peng Hu, J Paul Finn, et al.
Journal of the Royal Society, Interface|August 20, 2020
Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networksArghavan Arafati, Daisuke Morisawa, Michael R Avendi, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|January 15, 2005
Power optimization of wireless media systems with space-time block codesHomayoun Yousefi'zadeh, Hamid Jafarkhani, Mehran Moshfeghi
Medical Image Analysis|February 27, 2016
A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRIM R Avendi, Arash Kheradvar, Hamid Jafarkhani
Magnetic Resonance in Medicine|February 17, 2017
Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approachMichael R Avendi, Arash Kheradvar, Hamid Jafarkhani
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|April 2, 2014
Full-reference quality estimation for images with different spatial resolutionsAli Murat Demirtas, Amy R Reibman, Hamid Jafarkhani
IEEE Transactions on Bio-Medical Engineering|April 6, 2016
A 3-D Active Contour Method for Automated Segmentation of the Left Ventricle From Magnetic Resonance ImagesMahdi Hajiaghayi, Elliott M Groves, Hamid Jafarkhani, et al.
Future Cardiology|September 16, 2020
Prospect of artificial intelligence for the assessment of cardiac function and treatment of cardiovascular diseaseArash Kheradvar, Hamid Jafarkhani, Thomas Sloane Guy, et al.
Journal of Cardiovascular Magnetic Resonance : Official Journal of the Society for Cardiovascular Magnetic Resonance|December 1, 2020
Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseasesSaeed Karimi-Bidhendi, Arghavan Arafati, Andrew L Cheng, et al.
Cardiovascular Diagnosis and Therapy|November 19, 2019
Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical needArghavan Arafati, Peng Hu, J Paul Finn, et al.
Journal of the Royal Society, Interface|August 20, 2020
Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networksArghavan Arafati, Daisuke Morisawa, Michael R Avendi, et al.
Pageof 1