加强机器学习在医学影像中的研究和应用
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摘要
在临床工作中,医学影像为临床决策提供重要的辅助信息。但传统的影像诊断主要基于放射科医生的主观判断,已不能满足精准医学发展的要求。近年来,以深度学习为代表技术的机器学习方法,为拓展医学影像的临床应用范围提供了巨大的机遇。对机器学习在医学影像中的研究和应用、基本概念、研究现状作简要介绍,以期推动相关研究的开展。
In routine clinical practice, medical imaging has been intensively used to aid in decision-making.Traditional radiology diagnosis is made based on the radiologists' subjective determination, which could not meet the requirement raised by precise medicine. Recently, the development of machine learning, such as deep learning, provides a huge opportunity to speed up the development of traditional medical imaging. Introducing the basic concept, state of the art and clinical application of machine learning in medical imaging, and it is expected to promote the development in this field.
In routine clinical practice, medical imaging has been intensively used to aid in decision-making.Traditional radiology diagnosis is made based on the radiologists' subjective determination, which could not meet the requirement raised by precise medicine. Recently, the development of machine learning, such as deep learning, provides a huge opportunity to speed up the development of traditional medical imaging. Introducing the basic concept, state of the art and clinical application of machine learning in medical imaging, and it is expected to promote the development in this field.
引文
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[13]潘亚玲,王晗琦,陆勇.人工智能在医学影像CAD中的应用[J].国际医学放射学杂志,2019.DOI:10.19300/j.2019.Z6565zt.
[2]Kononenko I.Machine learning for medical diagnosis:history,state of the art and perspective[J].Artif Intell Med,2001,23:89-109.
[3]Cruz JA,Wishart DS.Applications of machine learning in cancer prediction and prognosis[J].Cancer Inform,2007,2:59-77.
[4]Powell S,Magnotta VA,Johnson H,et al.Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures[J].Neuroimage,2008,39:238-247.
[5]https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm604357.htm
[6]刘文广,谢斯敏,周雅芳,等.机器学习及其在肝脏疾病影像诊断中的研究进展[J].国际医学放射学杂志,2019.DOI:10.19300/j.2019.Z6102zt.
[7]黄斌,廖峰,叶裕丰,等.机器学习在鼻咽癌影像分析中的研究进展[J].国际医学放射学杂志,2019.DOI:10.19300/j.2019.Z6242zt.
[8]Kooi T,Litjens G,van Ginneken B,et al.Large scale deep learning for computer aided detection of mammographic lesions[J].Med Image Anal,2017,35:303-312.
[9]Gulshan V,Peng L,Coram M,et al.Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J].JAMA,2016,316:2402-2410.
[10]Esteva A,Kuprel B,Novoa RA,et al.Dermatologist-level classification of skin cancer with deep neural networks[J].Nature,2017,542:115-118.
[11]肖焕辉,袁程朗,冯仕庭,等.基于深度学习的癌症计算机辅助分类诊断研究进展[J].国际医学放射学杂志,2019.DOI:10.19300/j.2019.Z6366zt.
[12]潘德润,秦耿耿,陈卫国.基于深度学习的人工智能技术在乳腺癌筛查及影像诊断中的应用进展[J].国际医学放射学杂志,2019.DOI:10.19300/j.2019.Z6394zt.
[13]潘亚玲,王晗琦,陆勇.人工智能在医学影像CAD中的应用[J].国际医学放射学杂志,2019.DOI:10.19300/j.2019.Z6565zt.