留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

深度学习技术在医学影像领域的应用

夏黎明 沈坚 张荣国 王少康 陈宽

夏黎明, 沈坚, 张荣国, 王少康, 陈宽. 深度学习技术在医学影像领域的应用[J]. 协和医学杂志, 2018, 9(1): 10-14. doi: 10.3969/j.issn.1674-9081.2018.01.003
引用本文: 夏黎明, 沈坚, 张荣国, 王少康, 陈宽. 深度学习技术在医学影像领域的应用[J]. 协和医学杂志, 2018, 9(1): 10-14. doi: 10.3969/j.issn.1674-9081.2018.01.003
Li-ming XIA, Jian SHEN, Rong-guo ZHANG, Shao-kang WANG, Kuan CHEN. Application of Deep Learning in Medical Imaging Research[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(1): 10-14. doi: 10.3969/j.issn.1674-9081.2018.01.003
Citation: Li-ming XIA, Jian SHEN, Rong-guo ZHANG, Shao-kang WANG, Kuan CHEN. Application of Deep Learning in Medical Imaging Research[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(1): 10-14. doi: 10.3969/j.issn.1674-9081.2018.01.003

深度学习技术在医学影像领域的应用

doi: 10.3969/j.issn.1674-9081.2018.01.003
详细信息
    通讯作者:

    沈坚 电话:010-85795622, E-mail:contact@infervision.com

  • 中图分类号: R445

Application of Deep Learning in Medical Imaging Research

More Information
  • 摘要: 深度学习技术, 作为最近几年人工智能最热门的研究领域, 已成为全世界关注的焦点。深度学习在很多行业中展现出强大的应用能力, 在某些视听识别任务中的表现甚至超越了人类。在医学领域, 深度学习也逐渐成为研究者们分析大数据, 尤其是医学影像的首选方法。本文简要介绍深度学习的历史与概况, 结合国内外最新和最有影响力的研究成果, 阐述深度学习在医学影像领域的科学研究进展, 同时介绍深度学习在医学影像领域产品化应用及其未来的机遇与挑战。
  • [1] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks[J]. Science, 2016, 313:504-507. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4bf28668b4ac9aa4374f7393e37d2e9d
    [2] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521:436-444. doi:  10.1038/nature14539
    [3] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classifica-tion with deep convolutional neural networks[C]. Interna-tional Conference on Neural Information Processing Systems, 2012: 1097-1105.
    [4] Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017, 42:60-88. doi:  10.1016/j.media.2017.07.005
    [5] van Grinsven MJ, van Ginneken B, Hoyng CB, et al. Fast convolutional neural network training using selective data sampling:application to hemorrhage detection in color fundus images[J]. IEEE Trans Med Imaging, 2016, 35:1273-1284. doi:  10.1109/TMI.2016.2526689
    [6] Lin Y, Goyal P, Girshick R, et al. Focal loss for dense ob-ject detection[C]. In ICCV, 2017.
    [7] Ronneberger O, Philipp F, Thomas B. U-net: Convolutional networks for biomedical image segmentation[C].International Conference on Medical Image Computing and Computer Assisted Intervention, 2015.
    [8] Özgün C, Abdulkadir A, Lienkamp SS, et al. 3D U-net: learning dense volumetric segmentation from sparse annotation[C]. International Conference on Medical Image Computing and Computer Assisted Intervention, 2016.
    [9] Hazlett HC, Gu H, Munsell BC, et al. Early brain development in infants at high risk for autism spectrum disorder[J]. Nature, 2017, 542:348-351. doi:  10.1038/nature21369
    [10] 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, 2016316:2402-2410. doi:  10.1001/jama.2016.17216
    [11] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[J]. Computer Science, 2015:2818-2826. doi:  10.1109/CVPR.2016.308
    [12] Christian S, Sergey I, Vincent V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning[C]. AAAI, 2017.
    [13] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014, 1556:1409. https://arxiv.org/abs/1409.1556
    [14] 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. doi:  10.1016/j.media.2016.07.007
    [15] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
    [16] Lotter, William, Greg S, et al. A multi-scale CNN and curriculum learning strategy for mammogram classification[C]. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2017: 169-177.
    [17] Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542:115. doi:  10.1038/nature21056
  • 加载中
计量
  • 文章访问数:  413
  • HTML全文浏览量:  36
  • PDF下载量:  1936
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-10-11
  • 刊出日期:  2018-01-30

目录

    /

    返回文章
    返回

    【温馨提醒】近日,《协和医学杂志》编辑部接到作者反映,有多名不法人员冒充期刊编辑发送见刊通知,鼓动作者添加微信,从而骗取版面费的行为。特提醒您,本刊与作者联系的方式均为邮件通知或电话,稿件进度通知邮箱为:mjpumch@126.com,编辑部电话为:010-69154261,请提高警惕,谨防上当受骗!如有任何疑问,请致电编辑部核实。谢谢!