.png){kind=icon}
返回
Artificial intelligence applications for thoracic imaging
.png){kind=icon}
OA
AI
DOI：10.1016/j.ejrad.2019.108774.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural networks (CNNs), have led to substantial performance gain over the classic machine learning techniques. Multiple usages are currently being evaluated, especially for thoracic imaging, such as such as lung nodule evaluation, tuberculosis or pneumonia detection or quantification of diffuse lung diseases. Chest radiography is a near perfect domain for the development of deep learning algorithms for automatic interpretation, requiring large annotated datasets, in view of the high number of procedures and increasing data availability. Current algorithms are able to detect up to 14 common anomalies, when present as isolated findings. Chest computed tomography is another major field of application for artificial intelligence, especially in the perspective of large scale lung cancer screening. It is important for radiologists to apprehend, contribute actively and lead this new era of radiology powered by artificial intelligence. Such a perspective requires understanding new terms and concepts associated with machine learning. The objective of this paper is to provide useful definitions for understanding the methods used and their possibilities, and report current and future developments for thoracic imaging. Prospective validation of AI tools will be required before reaching routine clinical implementation.
Keyword:
Artificial intelligence
Deep learning
Machine learning
Thoracic imaging
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
1.1W
被引数:
1.8W
机构
引用论文
Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs基于深度学习的胸部主要疾病自动检测算法的开发和验证.png)
JAMA NETWORK OPENOAAI
JAMA NETWORK OPENIF9.7
OAAI分享收藏Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer体细胞突变驱动肺癌的不同成像表型CANCER RESEARCHOAAI
CANCER RESEARCHIF16.6
OAAI分享收藏分享收藏Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists胸部x光片诊断的深度学习: CheXNeXt算法与实践放射科医生的回顾性比较PLOS MEDICINEOAAI
PLOS MEDICINEIF9.9
OAAI分享收藏
查看更多内容