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In Neuroimaging clinics of North America

Deep learning has contributed to solving complex problems in science and engineering. This article provides the fundamental background required to understand and develop deep learning models for medical imaging applications. The authors review the main deep learning architectures such as multilayer perceptron, convolutional neural networks, autoencoders, recurrent neural networks, and generative adversarial neural networks. They also discuss the strategies for training deep learning models when the available datasets are imbalanced or of limited size and conclude with a discussion of the obstacles and challenges hindering the deployment of deep learning solutions in clinical settings.

Le William Trung, Maleki Farhad, Romero Francisco Perdigón, Forghani Reza, Kadoury Samuel


Convolutional neural network, Deep learning, Health care, Image registration, Image synthesis, Medical imaging, Radiology, Treatment planning