Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.
Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.
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DOI：
10.1007/s10278-017-9976-3
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发表时间：
2017-08
影响因子：
4.4
通讯作者：
Geis JR
中科院分区：
文献类型：
--
作者：
Kohli MD;Summers RM;Geis JR
在2016年9月举行的第一届医学成像机器智能年会（C-MIMI）上，一场关于医学图像数据和机器学习数据集的会议确定了多个问题。与会者的共同主题是，使用机器学习参与医学图像评估的每个人都缺乏数据。迫切需要找到更好的方法来收集、注释和重用医学成像数据。医学图像数据集的独特领域问题需要进一步研究、开发和传播最佳实践和标准，并需要医学成像领域专家、医学成像信息学家、政府和行业数据科学家以及感兴趣的商业、学术和政府实体之间的协调努力。应该更好地描述适合训练、测试、验证、验证和规范ML产品的可重用医学图像数据集的高级属性。NIH和其他政府机构应该促进并在适用的情况下强制使用医学图像数据集。我们应该加强医学成像领域专家、医学成像信息学家、临床和基础科学学术研究人员、政府和行业数据科学家以及感兴趣的商业实体之间的沟通。
At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities.
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