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山东大学学报 (医学版) ›› 2023, Vol. 61 ›› Issue (12): 21-29.doi: 10.6040/j.issn.1671-7554.0.2023.0774

• 医学影像人工智能的创新与挑战—专家综述 • 上一篇    下一篇

深度学习在医学影像学中的研究现状及发展前景

林冰洁1,2,王梅云1,2,3,*()   

  1. 1. 郑州大学人民医院医学影像科，河南 郑州 450003
    2. 河南省人民医院医学影像科，河南 郑州 450003
    3. 河南省科学院生物医学研究所 脑科学与类脑智能实验室，河南 郑州 450003
  • 收稿日期:2023-09-01 出版日期:2023-12-10 发布日期:2024-01-11
  • 通讯作者: 王梅云 E-mail:mywang@ha.edu.cnm
  • 作者简介:王梅云，主任医师、二级教授、博士研究生导师，河南省人民医院副院长、医学影像研究所所长、医学影像科主任，河南省科学院生物医学研究所所长、郑州大学医学技术学院学术副院长，美国哈佛大学医学院引进回国人才，长期致力于医学影像新技术研发及在重大疾病诊疗中的转化。学术兼职：中国放射学界首位美国医学与生物工程院(AIMBE)Fellow、国际医学磁共振学会(ISMRM)Fellow(均为2022年唯一入选的中国医生)，国内首位国际神经血管疾病学会(ISNVD)主席、国际医学磁共振学会(ISMRM)理事会理事及精神成像学组主席；中华医学会放射学分会常务委员、国际交流工作组组长及神经学组副组长，河南省放射学会候任主任委员等。荣誉称号：获得国家百千万人才工程“有突出贡献中青年专家”、国务院政府特殊津贴专家、全国科技系统抗击新冠肺炎疫情先进个人、中原学者、河南省优秀专家等荣誉称号。在国际顶级期刊JAMA、Nature Medicine、Nature Genetics、Nature Communications和放射学领域排名第一期刊Radiology等权威期刊上发表论文300余篇，3篇论文入选全球Top 1% ESI高被引论文；主持国家重点研发计划“诊疗装备与生物医用材料”重点专项及政府间合作重点专项、国家自然科学基金重点国际合作项目、面上项目等科研项目19项；牵头发布全国团体标准7项；以第一发明人获得授权美国发明专利和中国国家发明专利13项；获得河南省科技进步奖一等奖3项、中国发明创业奖创新奖一等奖1项和国际华人医学磁共振学会突出贡献奖等奖项；受邀在国际一流会议或学术机构如哈佛大学、剑桥大学等做特邀英文讲座50余次
  • 基金资助:
    河南省医学科技攻关计划项目(SBGJ202101002)

Research status and development prospect of deep learning in medical imaging

Bingjie LIN1,2,Meiyun WANG1,2,3,*()   

  1. 1. Department of Medical Imaging, Zhengzhou University People's Hospital, Zhengzhou 450003, Henan, China
    2. Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou 450003, Henan, China
    3. Laboratory of Brain Science and Brain-like Intelligence, Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou 450003, Henan, China
  • Received:2023-09-01 Online:2023-12-10 Published:2024-01-11
  • Contact: Meiyun WANG E-mail:mywang@ha.edu.cnm

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摘要：

精准医疗，影像先行；精准影像，技术先行。近年来，随着人工智能的迅速发展，深度学习作为其重要分支，已广泛应用于信号处理、计算机视觉和自然语言处理等诸多领域，其中基于深度学习的医学影像数据分割、疾病检测及预后预测等已成为众多学者研究的热点。本文将简要概述深度学习在医学影像学主要技术领域的应用现状，并分析其在医学影像学临床应用中所面临的挑战与发展前景，旨在为深度学习算法的临床转化提供参考。

关键词: 深度学习, 医学影像学, 研究现状, 发展前景

Abstract:

Precision medicine, imaging first; precision imaging, technology first. In recent years, with the rapid development of artificial intelligence, deep learning, as an important branch, has been widely used in many fields such as signal processing, computer vision and natural language processing, etc., among which medical image data segmentation, disease detection and prognosis prediction based on deep learning have become the hot spots of many scholars' research. In this paper, we will briefly outline the current status of deep learning application in the main technical fields of medical imaging, and analyze the challenges and development prospects of its clinical application in medical imaging, aiming to provide reference for the transformation of deep learning algorithms in the clinic.

Key words: Deep learning, Medical imaging, Research status, Development prospect

中图分类号: 

  • R445
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