A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop
A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop
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DOI：
10.1148/radiol.2019190613
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发表时间：
2019-06-01
期刊：
影响因子：
19.7
通讯作者：
Kandarpa, Krishna
中科院分区：
文献类型：
--
作者：
Langlotz, Curtis P.;Allen, Bibb;Kandarpa, Krishna
成像研究实验室正在迅速创建机器学习系统，使用开放源代码的方法和工具实现专业的人类表现。这些人工智能系统正在开发，以改进医学图像重建、降噪、质量保证、分类、分割、计算机辅助检测、计算机辅助分类和放射基因组学。2018年8月，在马里兰州贝塞斯达的国立卫生研究所举行了一次会议，讨论当前的技术水平和知识差距，并为未来的研究举措制定路线图。主要研究重点包括：1，从源数据高效地产生适合人类解释的图像的新的图像重建方法；2，自动图像标记和注释方法，包括从成像报告中提取信息，电子表型，以及预期的结构化图像报告；3，临床成像数据的新的机器学习方法，例如定制的、预先训练的模型架构，以及联合机器学习方法；4，能够解释它们向人类用户提供的建议的机器学习方法(所谓的可解释人工智能)；以及5，经过验证的图像去识别和数据共享方法，以促进临床成像数据集的广泛获得。这份研究路线图旨在确定学术研究实验室、资助机构、专业协会和行业的这些需求并确定其优先顺序。(C)RSNA，2019年
Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry. (C) RSNA, 2019