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.
Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and Research Institute of the McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada.
Neuroimaging Clin N Am. 2020 Nov;30(4):417-431. doi: 10.1016/j.nic.2020.06.003. Epub 2020 Sep 18.
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.
深度学习在解决科学和工程领域的复杂问题方面做出了贡献。本文提供了理解和开发医学成像应用深度学习模型所需的基本背景知识。作者回顾了主要的深度学习架构，如多层感知器、卷积神经网络、自动编码器、递归神经网络和生成对抗神经网络。他们还讨论了在可用数据集不平衡或规模有限的情况下训练深度学习模型的策略，并最后讨论了阻碍深度学习解决方案在临床环境中部署的障碍和挑战。
