Neuroimaging clinics of North America
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewMachine Learning Algorithm Validation: From Essentials to Advanced Applications and Implications for Regulatory Certification and Deployment.
The deployment of machine learning (ML) models in the health care domain can increase the speed and accuracy of diagnosis and improve treatment planning and patient care. Translating academic research to applications that are deployable in clinical settings requires the ability to generalize and high reproducibility, which are contingent on a rigorous and sound methodology for the development and evaluation of ML models. This article describes the fundamental concepts and processes for ML model evaluation and highlights common workflows. It concludes with a discussion of the requirements for the deployment of ML models in clinical settings.
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewOverview of Machine Learning Part 1: Fundamentals and Classic Approaches.
The extensive body of research and advances in machine learning (ML) and the availability of a large volume of patient data make ML a powerful tool for producing models with the potential for widespread deployment in clinical settings. This article provides an overview of the classic supervised and unsupervised ML methods as well as fundamental concepts required for understanding how to develop generalizable and high-performing ML applications. It also describes the important steps for developing a ML model and how decisions made in these steps affect model performance and ability to generalize.
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewArtificial Intelligence and Stroke Imaging: A West Coast Perspective.
Artificial intelligence (AI) advancements have significant implications for medical imaging. Stroke is the leading cause of disability and the fifth leading cause of death in the United States. ⋯ AI techniques are well-suited for dealing with vast amounts of stroke imaging data and a large number of multidisciplinary approaches used in classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. This article addresses this topic and seeks to present an overview of machine learning and/or deep learning applied to stroke imaging.
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Natural language processing (NLP) is an interdisciplinary field, combining linguistics, computer science, and artificial intelligence to enable machines to read and understand human language for meaningful purposes. Recent advancements in deep learning have begun to offer significant improvements in NLP task performance. These techniques have the potential to create new automated tools that could improve clinical workflows and unlock unstructured textual information contained in radiology and clinical reports for the development of radiology and clinical artificial intelligence applications. These applications will combine the appropriate application of classic linguistic and NLP preprocessing techniques, modern NLP techniques, and modern deep learning techniques.
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Neuroimaging Clin. N. Am. · Nov 2020
Review Comparative StudyKnowledge Based Versus Data Based: A Historical Perspective on a Continuum of Methodologies for Medical Image Analysis.
The advent of big data and deep learning algorithms has promoted a major shift toward data-driven methods in medical image analysis recently. However, the medical image analysis field has a long and rich history inclusive of both knowledge-driven and data-driven methodologies. In the present article, we provide a historical review of an illustrative sample of medical image analysis methods and locate them along a knowledge-driven versus data-driven continuum. In doing so, we highlight the historical importance as well as current-day relevance of more traditional, knowledge-based artificial intelligence approaches and their complementarity with fully data-driven techniques such as deep learning.