Machine Learning for the Interventional Radiologist

Meek Ryan D.; Lungren Matthew P.; Gichoya Judy W.

首页> 外文期刊>AJR: American Journal of Roentgenology : Including Diagnostic Radiology, Radiation Oncology, Nuclear Medicine, Ultrasonography and Related Basic Sciences >Machine Learning for the Interventional Radiologist

【24h】

Machine Learning for the Interventional Radiologist

机译：用于介入放射学家的机器学习

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

OBJECTIVE. The purpose of this article is to describe key potential areas of application of machine learning in interventional radiology.

机译：客观的。本文的目的是描述介入放射学中机器学习的关键潜在领域。

著录项

来源
《AJR: American Journal of Roentgenology : Including Diagnostic Radiology, Radiation Oncology, Nuclear Medicine, Ultrasonography and Related Basic Sciences》 |2019年第4期|共3页
作者
Meek Ryan D.; Lungren Matthew P.; Gichoya Judy W.;
展开▼
作者单位

Oregon Hlth &

Sci Univ Charles T Dotter Dept Intervent Radiol 3181 SW Sam Jackson Pk Rd Portland;

Stanford Univ Sch Med Dept Radiol Stanford CA 94305 USA;

Oregon Hlth &

Sci Univ Charles T Dotter Dept Intervent Radiol 3181 SW Sam Jackson Pk Rd Portland;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类放射医学;
关键词
artificial intelligence; interventional radiology; machine learning;

机译：人工智能;介入放射学;机器学习;

相似文献

外文文献
中文文献
专利

1. Machine Learning for the Interventional Radiologist [J] . Meek Ryan D., Lungren Matthew P., Gichoya Judy W. AJR: American Journal of Roentgenology : Including Diagnostic Radiology, Radiation Oncology, Nuclear Medicine, Ultrasonography and Related Basic Sciences . 2019,第4期

机译：用于介入放射学家的机器学习
2. Unique Learning System for Uterine Artery Embolization for Symptomatic Myoma and Adenomyosis for Obstetrician-Gynecologists in Cooperation with Interventional Radiologists: Evaluation of UAE From the Point of View of Gynecologists Who Perform UAE [J] . Shu Soeda, Tsuyoshi Hiraiwa, Megumi Takata, Journal of minimally invasive gynecology . 2018,第1期

机译：与介入放射科学家合作的症状肌瘤和产科妇科医生对症状性肌瘤和腺瘤症的独特学习系统：从表演阿联酋的妇科医生的角度评估阿联酋
3. Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images [J] . Sha-Sha Zhao, Xiu-Long Feng, Yu-Chuan Hu, BMC Neurology . 2020,第1期

机译：在与传统T1对比度增强和流体减毒的反转恢复图像中，在机器学习中与机器学习的III oligodendrogliomas的II级和III oligodendrogliomas的级别更好的疗效
4. Machine Learning Based Common Radiologist-Level Pneumonia Detection on Chest X-rays [C] . Min-Jen Tsai, Yu-Han Tao International Conference on Signal Processing and Communication Systems . 2019

机译：基于机器学习的胸部X射线普通放射线水平的肺炎检测
5. Development of Eye Dosimeter Using Additive Manufacturing Techniques to Monitor Occupational Eye Lens Exposures to Interventional Radiologists [D] . Choi, JungHwan. 2017

机译：使用添加剂制造技术的眼剂量表的开发来监测职业眼镜曝光到介入放射液
6. Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists [O] . Muhammad Attique Khan, Imran Ashraf, Majed Alhaisoni, 2020

机译：使用深度学习和强大的特征选择的多模式脑肿瘤分类：放射科学家的机器学习应用
7. What Your Radiologist Might be Missing: Using Machine Learning to Identify Mislabeled Instances of X-ray Images [O] . Tim Rädsch, Sven Eckhardt, Florian Leiser, 2021

机译：您的放射科医师可能缺失是什么：使用机器学习来识别X射线图像的错误标记实例

Machine Learning for the Interventional Radiologist

摘要

著录项

相似文献

相关主题

期刊订阅