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Mariam Fouad
Also published under:Mariam M. Fouad
Affiliation
Chair for Medical Engineering, Ruhr University Bochum, Bochum, Germany
Topic
B-mode Images,Harmonic Imaging,Ultrasound Imaging,Frame Rate,Convolutional Layers,Decoding Path,Amplitude Modulation,Contrast Ratio,Convolutional Autoencoder,Deep Learning,Local Mean,Local Standard Deviation,Motion Artifacts,Plane Wave,Spectral Leakage,Steering Angle,Aperture,Architecture Implementation,Data Augmentation,Data Harmonization,Deep Neural Network,Echo Signal,Generative Adversarial Networks,High Frame Rate,High-intensity Regions,Leaky ReLU,Learning Rate,Line Scan,Resonance Effect,Single-shot Imaging,Training Set,Transfer Learning,Water Bath,Activation Function,Air Bubbles,Angle Error,Atrous Convolution,Attention Mechanism,Attention Module,Autoencoder Architecture,Beamforming,Binary Classification,Binary Classification Task,Blood Glucose Levels,Cannula Tip,Class Imbalance,Class Weights,Classification Head,Classification Task,Consistency Regularization,
Biography
Mariam Fouad was born in Cairo, Egypt, in 1993. She received the bachelor’s degree in electronics engineering and the master’s degree from German University in Cairo (GUC), New Cairo, Egypt, in 2015 and 2017, respectively. She is currently pursuing the Ph.D. degree with the Department of Medical Engineering, Ruhr University Bochum, Bochum, Germany, in collaboration with GUC.
Her current research interests include the utilization of deep learning concepts in ultrasound special applications such as harmonic imaging, cannula visualization, and synthetic data generation.
Her current research interests include the utilization of deep learning concepts in ultrasound special applications such as harmonic imaging, cannula visualization, and synthetic data generation.