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Karim Armanious
Also published under:
Affiliation
Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
Topic
Convolutional Neural Network,Generative Adversarial Networks,Magnetic Resonance Imaging,Loss Function,Age Estimation,Brain Aging,Brain Magnetic Resonance Imaging,Chronological Age,Conditional Generative Adversarial Network,Deep Learning,Deep Neural Network,Magnetic Resonance Imaging Scans,Senescence,Training Dataset,Background Noise,Biological Age Estimation,Clinical Dementia Rating,Convolutional Layers,Deep Convolutional Neural Network,Deep Learning Approaches,General Architecture,Gray Matter,Ground Truth Labels,Human Speech,Impairment In Alzheimer,Iterative Scheme,L1 Loss,Magnetic Resonance Imaging Datasets,Magnetic Resonance Scanner,Mean Opinion Score,Mild Dementia,Model-based Approach,Moderate Dementia,Organ Systems,Outlier Detection,Prediction Uncertainty,Recurrent Neural Network,Root Mean Square Error,Short-time Fourier Transform,Speech Signal,Translational Approach,Word Error Rate,3D Network,3D Volume,Adaptive Method,Additional Loss,Advances In Deep Learning,Aleatoric Uncertainty,Arbitrary Region,Artifact Correction,
Biography
Karim Armanious received the M.Sc. degree in electrical engineering and information technology from the University of Stuttgart, Stuttgart, Germany, in 2016, where he is currently working toward the Ph.D. degree in electrical engineering and information technology.
His research interests include deep learning, generative adversarial networks, and machine learning with application to radar, acoustics, and medical imaging.
His research interests include deep learning, generative adversarial networks, and machine learning with application to radar, acoustics, and medical imaging.