无数学原理的机器学习概论：放射科医师指南。
A No-Math Primer on the Principles of Machine Learning for Radiologists.
机构信息
Department of Radiology, NYU Grossman School of Medicine, New York, NY.
Department of Radiology, NYU Grossman School of Medicine, New York, NY; Courant Institute of Mathematical Sciences, New York University, New York, NY.
出版信息
Semin Ultrasound CT MR. 2022 Apr;43(2):133-141. doi: 10.1053/j.sult.2022.02.002. Epub 2022 Feb 11.
Abstract
Machine learning is becoming increasingly important in both research and clinical applications in radiology due to recent technological developments, particularly in deep learning. As these technologies are translated toward clinical practice, there is a need for radiologists and radiology trainees to understand the basic principles behind them. This primer provides an accessible introduction to the vocabulary and concepts that are central to machine learning and relevant to the radiologist.
摘要
由于最近的技术发展，特别是深度学习，机器学习在放射学的研究和临床应用中变得越来越重要。随着这些技术向临床实践的转化，放射科医生和放射科培训生需要了解其背后的基本原理。本入门读物提供了一个易于理解的介绍，介绍了机器学习的核心词汇和概念，以及与放射科医生相关的内容。
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