Overview of Machine Learning: Part 2

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.

Source: Neuroimaging Clinics - September 17, 2020 Category: Radiology Authors: William Trung Le, Farhad Maleki, Francisco Perdig ón Romero, Reza Forghani, Samuel Kadoury Source Type: research