Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences
Author:
Affiliation:
1. Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA
2. Scientific Computing and Imaging Institute (SCI), University of Utah, Salt Lake City, UT, USA
Funder
NIH the National Institute of General Medical Sciences (NIGMS) Center for Integrative Biomedical Computing
NIH the National Heart, Lung, and Blood Institute
NSF CAREER Award
NIH NIGMS
Nora Eccles Treadwell Foundation for Cardiovascular Research
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software
Link
https://ieeexplore.ieee.org/ielam/42/10035777/9932432-aam.pdf
Reference49 articles.
1. Deep learning neural nets for detecting heart activity;horvath;arXiv 1901 09831,2019
2. A Spatial Adaptation of the Time Delay Neural Network for Solving ECGI Inverse Problem
3. Geometric Deep Learning: Going beyond Euclidean data
4. Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
5. A machine learning approach to reconstruction of heart surface potentials from body surface potentials
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