相似文献/References:
[1]陶源,王佳飞,杜俊龙,等.基于卷积神经网络的细胞识别[J].中国医学物理学杂志,2017,34(1):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[J].Chinese Journal of Medical Physics,2017,34(8):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[2]谭泓叶,张晓红,张海黔.银纳米粒子对乏氧胶质瘤细胞的影响[J].中国医学物理学杂志,2017,34(1):89.[doi:10.3969/j.issn.1005-202X.2017.01.018]
[J].Chinese Journal of Medical Physics,2017,34(8):89.[doi:10.3969/j.issn.1005-202X.2017.01.018]
[3]门阔,戴建荣. 利用深度反卷积神经网络自动勾画放疗危及器官[J].中国医学物理学杂志,2018,35(3):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
MEN Kuo,DAI Jianrong. Automatic segmentation of organs at risk in radiotherapy using deep deconvolutional neural network[J].Chinese Journal of Medical Physics,2018,35(8):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
[4]张国前,张书旭,王锐濠,等. Auto-planning在脑胶质瘤非共面容积调强放疗计划中的应用[J].中国医学物理学杂志,2018,35(5):514.[doi:DOI:10.3969/j.issn.1005-202X.2018.05.004]
ZHANG Guoqian,ZHANG Shuxu,WANG Ruihao,et al. Application research on Auto-planning in non-coplanar VMAT plan for brain gliomas[J].Chinese Journal of Medical Physics,2018,35(8):514.[doi:DOI:10.3969/j.issn.1005-202X.2018.05.004]
[5]邓金城,彭应林,刘常春,等. 深度卷积神经网络在放射治疗计划图像分割中的应用[J].中国医学物理学杂志,2018,35(6):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
DENG Jincheng,PENG Yinglin,LIU Changchun,et al. Application of deep convolution neural network in radiotherapy planning image segmentation[J].Chinese Journal of Medical Physics,2018,35(8):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
[6]查雪帆,杨丰,吴俣南,等. 结合迁移学习与深度卷积网络的心电分类研究[J].中国医学物理学杂志,2018,35(11):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
ZHA Xuefan,YANG Feng,WU Yunan,et al. ECG classification based on transfer learning and deep convolution neural network[J].Chinese Journal of Medical Physics,2018,35(8):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
[7]宫进昌,赵尚义,王远军. 基于深度学习的医学图像分割研究进展[J].中国医学物理学杂志,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
GONG Jinchang,ZHAO Shangyi,WANG Yuanjun.Research progress on deep learning-based medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(8):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[8]安莹,黄能军,杨荣,等. 基于深度学习的心血管疾病风险预测模型[J].中国医学物理学杂志,2019,36(9):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
AN Ying,HUANG Nengjun,YANG Rong,et al. Deep learning-based model for risk prediction of cardiovascular diseases[J].Chinese Journal of Medical Physics,2019,36(8):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
[9]徐航,随力,张靖雯,等.卷积神经网络在医学图像分割中的研究进展[J].中国医学物理学杂志,2019,36(11):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
XU Hang,SUI Li,ZHANG Jingwen,et al.Progress on convolutional neural network in medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(8):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
[10]张富利,崔德琪,王秋生,等.基于深度学习和图谱库方法自动勾画肿瘤放疗中危及器官的比较[J].中国医学物理学杂志,2019,36(12):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
ZHANG Fuli,CUI Deqi,WANG Qiusheng,et al.Comparative study of deep learning- versus Atlas-based auto-segmentation of organs-at-risk in tumor radiotherapy[J].Chinese Journal of Medical Physics,2019,36(8):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]