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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.

DOI:10.1053/j.sult.2022.02.002
PMID:35339253
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9363000/
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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本文引用的文献

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Automated Radiology Alert System for Pneumothorax Detection on Chest Radiographs Improves Efficiency and Diagnostic Performance.用于胸部X光片气胸检测的自动放射学警报系统提高了效率和诊断性能。
Diagnostics (Basel). 2021 Jun 29;11(7):1182. doi: 10.3390/diagnostics11071182.
2
Not all biases are bad: equitable and inequitable biases in machine learning and radiology.并非所有偏差都是有害的:机器学习与放射学中的公平偏差与不公平偏差
Insights Imaging. 2021 Feb 10;12(1):13. doi: 10.1186/s13244-020-00955-7.
3
The future of digital health with federated learning.联合学习助力数字健康的未来。
NPJ Digit Med. 2020 Sep 14;3:119. doi: 10.1038/s41746-020-00323-1. eCollection 2020.
4
The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database.基于人工智能且获美国食品药品监督管理局批准的医疗设备及算法的现状:一个在线数据库。
NPJ Digit Med. 2020 Sep 11;3:118. doi: 10.1038/s41746-020-00324-0. eCollection 2020.
5
A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.一种全自动人工智能方法,用于非侵入性、基于成像的胶质母细胞瘤遗传改变识别。
Sci Rep. 2020 Jul 16;10(1):11852. doi: 10.1038/s41598-020-68857-8.
6
Training a neural network for Gibbs and noise removal in diffusion MRI.训练神经网络进行扩散 MRI 中的 Gibbs 噪声和噪声去除。
Magn Reson Med. 2021 Jan;85(1):413-428. doi: 10.1002/mrm.28395. Epub 2020 Jul 14.
7
Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations.将人工智能融入放射科的临床实践:挑战与建议。
Eur Radiol. 2020 Jun;30(6):3576-3584. doi: 10.1007/s00330-020-06672-5. Epub 2020 Feb 17.
8
Artificial Intelligence Explained for Nonexperts.面向非专业人士的人工智能解读
Semin Musculoskelet Radiol. 2020 Feb;24(1):3-11. doi: 10.1055/s-0039-3401041. Epub 2020 Jan 28.
9
Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.人工智能在放射学中的伦理:欧洲和北美多学会联合声明摘要。
J Am Coll Radiol. 2019 Nov;16(11):1516-1521. doi: 10.1016/j.jacr.2019.07.028. Epub 2019 Oct 1.
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Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa.基于 MRI 纹理特征的机器学习分析预测前置胎盘患者胎盘植入谱。
Magn Reson Imaging. 2019 Dec;64:71-76. doi: 10.1016/j.mri.2019.05.017. Epub 2019 May 15.