Neuroimaging clinics of North America
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewAn East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1: Hemorrhagic Stroke Imaging and Triage.
Hemorrhagic stroke is a medical emergency. Artificial intelligence techniques and algorithms may be used to automatically detect and quantitate intracranial hemorrhage in a semiautomated fashion. ⋯ This article reviews artificial intelligence algorithms for intracranial hemorrhage detection, quantification, and prognostication. Multiple algorithms currently being explored are described and illustrated with the help of examples.
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewMachine Learning Applications for Head and Neck Imaging.
The head and neck (HN) consists of a large number of vital anatomic structures within a compact area. Imaging plays a central role in the diagnosis and management of major disorders affecting the HN. ⋯ It categorizes ML applications in HN imaging into deep learning and traditional ML applications and provides examples of each category. It also discusses the main challenges facing the successful deployment of ML-based applications in the clinical setting and provides suggestions for addressing these challenges.
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewOverview 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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This article reviews the history of artificial intelligence and introduces the reader to major events that prompted interest in the field, as well as pitfalls and challenges that have slowed its development. The purpose of this article is to provide a high-level historical perspective on the development of the field over the past decades, highlighting the potential of the field for transforming health care, but also the importance of setting realistic expectations for artificial intelligence applications to avoid repeating historical cyclical trends and a third "artificial intelligence winter."