Overview of Machine Learning: Part 2: Deep Learning for Medical

Neuroimaging Clin. N. Am. · Nov 2020

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Overview of Machine Learning: Part 2: Deep Learning for Medical Image Analysis.

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.

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- William Trung Le, Farhad Maleki, Francisco Perdigón Romero, Reza Forghani, and Samuel Kadoury.
- Polytechnique Montreal, PO Box 6079, succ. Centre-ville, Montreal, Quebec H3C 3A7, Canada; CHUM Research Center, 900 St Denis Street, Montreal, Quebec H2X 0A9, Canada.
- Neuroimaging Clin. N. Am. 2020 Nov 1; 30 (4): 417-431.
AbstractDeep 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.Copyright © 2020 Elsevier Inc. All rights reserved.

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Overview of Machine Learning: Part 2: Deep Learning for Medical Image Analysis.

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