[1]郭磊,王骏,丁维昌,等.4D卷积神经网络的自闭症功能磁共振图像分类[J].智能系统学报,2021,16(6):1021-1029.[doi:10.11992/tis.202009022]
 GUO Lei,WANG Jun,DING Weichang,et al.Classification of the functional magnetic resonance image of autism based on 4D convolutional neural network[J].CAAI Transactions on Intelligent Systems,2021,16(6):1021-1029.[doi:10.11992/tis.202009022]
点击复制

4D卷积神经网络的自闭症功能磁共振图像分类

参考文献/References:
[1] 张芬, 王穗苹, 杨娟华, 等. 自闭症谱系障碍者异常的大脑功能连接[J]. 心理科学进展, 2015, 23(7): 1196-1204
ZHANG Fen, WANG Suiping, YANG Juanhua, et al. Atypical brain functional connectivity in autism spectrum disorders[J]. Advances in psychological science, 2015, 23(7): 1196-1204
[2] CABALLERO-GAUDES C, REYNOLDS R C. Methods for cleaning the BOLD fMRI signal[J]. Neuroimage, 2017, 154: 128-149.
[3] MOELLER S, YACOUB E, OLMAN C A, et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain FMRI[J]. Magnetic resonance in medicine, 2010, 63(5): 1144-1153.
[4] 常春云. 基于静息态功能磁共振成像的自闭症预测研究[D]. 北京: 北京交通大学, 2017.
CHANG Chunyun. Research on prediction of autism spectrum disorder based on resting-state fMRI[D]. Beijing: Beijing Jiaotong University, 2017.
[5] DVORNEK N C, VENTOLA P, PELPHREY K A, et al. Identifying autism from resting-state fMRI using long short-term memory networks[M]//WANG Qian, SHI Yinghuan, SUK H I, et al. Machine Learning in Medical Imaging. Cham: Springer, 2017: 362-370.
[6] ZHAO Yu, DONG Qinglin, ZHANG Shu, et al. Automatic recognition of fMRI-derived functional networks using 3-D convolutional neural networks[J]. IEEE transactions on biomedical engineering, 2018, 65(9): 1975-1984.
[7] LI Xiaoxiao, DVORNEK N C, PAPADEMETRIS X, et al. 2-Channel convolutional 3D deep neural network (2CC3D) for fMRI analysis: ASD classification and feature learning[C]//2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Washington, USA, 2018: 1252-1255.
[8] KHOSLA M, JAMISON K, KUCEYESKI A, et al. Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction[J]. NeuroImage, 2019, 199: 651-662.
[9] LU Zhou, PU Hongming, WANG Feicheng, et al. The expressive power of neural networks: a view from the width[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA, 2017: 6232-6240.
[10] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, 2012: 1097-1105.
[11] JI Shuiwang, XU Wei, YANG Ming, et al. 3D Convolutional neural networks for human action recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(1): 221-231.
[12] DIBA A, FAYYAZ M, SHARMA V, et al. Temporal 3D convnets: new architecture and transfer learning for video classification [EB/OL].(2017-11-22)[2020-9-20]https:// arxiv. org/ abs/1711.08200.
[13] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[14] HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. Computer science, 2012, 3(4): 212-223.
[15] SIMONYAN K, ZISSERMAN A . Very deep convolutional networks for large-scale image recognition[EB/OL]// (2018-8-16)[2019-4-26]https://arxiv.org/abs/1808.05377v1.
[16] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA, 2014: 1-9.
[17] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on International Conference on Machine Learning. Lille, France, 2015: 448-456.
[18] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA, 2016: 2818-2826.
[19] SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, USA, 2017: 4278-4284.
[20] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA, 2016: 770-778.
[21] HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA, 2017: 2261-2269.
[22] EL GAZZAR A, CERLIANI L, VAN WINGEN G, et al. Simple 1-D convolutional networks for resting-state fMRI based classification in autism[C]//2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary, 2019: 1-6.
[23] YAN Chaogan, WANG Xindi, ZUO Xinian, et al. DPABI: data processing & analysis for (resting-state) brain imaging[J]. Neuroinformatics, 2016, 14(3): 339-351.
[24] HECHT-NIELSEN. Theory of the backpropagation neural network[C]//International 1989 Joint Conference on Neural Networks. Washington, USA, 1989: 593-605.
[25] ELSKEN T, METZEN J H, HUTTER F . Neural architecture search: A survey[EB/OL]. (2018-8-16)[2020-9-20]https://arxiv.org/abs/1808.05377v1.
[26] SALVADOR R, SUCKLING J, COLEMAN M R, et al. Neurophysiological architecture of functional magnetic resonance images of human brain[J]. Cerebral cortex, 2005, 15(9): 1332-1342.
相似文献/References:
[1]张媛媛,霍静,杨婉琪,等.深度信念网络的二代身份证异构人脸核实算法[J].智能系统学报,2015,10(2):193.[doi:10.3969/j.issn.1673-4785.201405060]
 ZHANG Yuanyuan,HUO Jing,YANG Wanqi,et al.A deep belief network-based heterogeneous face verification method for the second-generation identity card[J].CAAI Transactions on Intelligent Systems,2015,10():193.[doi:10.3969/j.issn.1673-4785.201405060]
[2]丁科,谭营.GPU通用计算及其在计算智能领域的应用[J].智能系统学报,2015,10(1):1.[doi:10.3969/j.issn.1673-4785.201403072]
 DING Ke,TAN Ying.A review on general purpose computing on GPUs and its applications in computational intelligence[J].CAAI Transactions on Intelligent Systems,2015,10():1.[doi:10.3969/j.issn.1673-4785.201403072]
[3]殷瑞,苏松志,李绍滋.一种卷积神经网络的图像矩正则化策略[J].智能系统学报,2016,11(1):43.[doi:10.11992/tis.201509018]
 YIN Rui,SU Songzhi,LI Shaozi.Convolutional neural network’s image moment regularizing strategy[J].CAAI Transactions on Intelligent Systems,2016,11():43.[doi:10.11992/tis.201509018]
[4]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报,2016,11(3):279.[doi:10.11992/tis.201603026]
 MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11():279.[doi:10.11992/tis.201603026]
[5]龚震霆,陈光喜,任夏荔,等.基于卷积神经网络和哈希编码的图像检索方法[J].智能系统学报,2016,11(3):391.[doi:10.11992/tis.201603028]
 GONG Zhenting,CHEN Guangxi,REN Xiali,et al.An image retrieval method based on a convolutional neural network and hash coding[J].CAAI Transactions on Intelligent Systems,2016,11():391.[doi:10.11992/tis.201603028]
[6]师亚亭,李卫军,宁欣,等.基于嘴巴状态约束的人脸特征点定位算法[J].智能系统学报,2016,11(5):578.[doi:10.11992/tis.201602006]
 SHI Yating,LI Weijun,NING Xin,et al.A facial feature point locating algorithmbased on mouth-state constraints[J].CAAI Transactions on Intelligent Systems,2016,11():578.[doi:10.11992/tis.201602006]
[7]马世龙,乌尼日其其格,李小平.大数据与深度学习综述[J].智能系统学报,2016,11(6):728.[doi:10.11992/tis.201611021]
 MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11():728.[doi:10.11992/tis.201611021]
[8]王亚杰,邱虹坤,吴燕燕,等.计算机博弈的研究与发展[J].智能系统学报,2016,11(6):788.[doi:10.11992/tis.201609006]
 WANG Yajie,QIU Hongkun,WU Yanyan,et al.Research and development of computer games[J].CAAI Transactions on Intelligent Systems,2016,11():788.[doi:10.11992/tis.201609006]
[9]黄心汉.A3I:21世纪科技之光[J].智能系统学报,2016,11(6):835.[doi:10.11992/tis.201605022]
 HUANG Xinhan.A3I: the star of science and technology for the 21st century[J].CAAI Transactions on Intelligent Systems,2016,11():835.[doi:10.11992/tis.201605022]
[10]杨晓兰,强彦,赵涓涓,等.基于医学征象和卷积神经网络的肺结节CT图像哈希检索[J].智能系统学报,2017,12(6):857.[doi:10.11992/tis.201706035]
 YANG Xiaolan,QIANG Yan,ZHAO Juanjuan,et al.Hashing retrieval for CT images of pulmonary nodules based on medical signs and convolutional neural networks[J].CAAI Transactions on Intelligent Systems,2017,12():857.[doi:10.11992/tis.201706035]
[11]刘帅师,程曦,郭文燕,等.深度学习方法研究新进展[J].智能系统学报,2016,11(5):567.[doi:10.11992/tis.201511028]
 LIU Shuaishi,CHENG Xi,GUO Wenyan,et al.Progress report on new research in deep learning[J].CAAI Transactions on Intelligent Systems,2016,11():567.[doi:10.11992/tis.201511028]
[12]宋婉茹,赵晴晴,陈昌红,等.行人重识别研究综述[J].智能系统学报,2017,12(6):770.[doi:10.11992/tis.201706084]
 SONG Wanru,ZHAO Qingqing,CHEN Changhong,et al.Survey on pedestrian re-identification research[J].CAAI Transactions on Intelligent Systems,2017,12():770.[doi:10.11992/tis.201706084]
[13]王科俊,赵彦东,邢向磊.深度学习在无人驾驶汽车领域应用的研究进展[J].智能系统学报,2018,13(1):55.[doi:10.11992/tis.201609029]
 WANG Kejun,ZHAO Yandong,XING Xianglei.Deep learning in driverless vehicles[J].CAAI Transactions on Intelligent Systems,2018,13():55.[doi:10.11992/tis.201609029]
[14]莫凌飞,蒋红亮,李煊鹏.基于深度学习的视频预测研究综述[J].智能系统学报,2018,13(1):85.[doi:10.11992/tis.201707032]
 MO Lingfei,JIANG Hongliang,LI Xuanpeng.Review of deep learning-based video prediction[J].CAAI Transactions on Intelligent Systems,2018,13():85.[doi:10.11992/tis.201707032]
[15]葛园园,许有疆,赵帅,等.自动驾驶场景下小且密集的交通标志检测[J].智能系统学报,2018,13(3):366.[doi:10.11992/tis.201706040]
 GE Yuanyuan,XU Youjiang,ZHAO Shuai,et al.Detection of small and dense traffic signs in self-driving scenarios[J].CAAI Transactions on Intelligent Systems,2018,13():366.[doi:10.11992/tis.201706040]
[16]汪鸿翔,柳培忠,骆炎民,等.高斯核函数卷积神经网络跟踪算法[J].智能系统学报,2018,13(3):388.[doi:10.11992/tis.201612040]
 WANG Hongxiang,LIU Peizhong,LUO Yanmin,et al.Convolutional neutral network tracking algorithm accelerated by Gaussian kernel function[J].CAAI Transactions on Intelligent Systems,2018,13():388.[doi:10.11992/tis.201612040]
[17]冯小荣,惠康华,柳振东.基于卷积特征和贝叶斯分类器的人脸识别[J].智能系统学报,2018,13(5):769.[doi:10.11992/tis.201706052]
 FENG Xiaorong,HUI Kanghua,LIU Zhendong.Face recognition based on convolution feature and Bayes classifier[J].CAAI Transactions on Intelligent Systems,2018,13():769.[doi:10.11992/tis.201706052]
[18]莫宏伟,汪海波.基于Faster R-CNN的人体行为检测研究[J].智能系统学报,2018,13(6):967.[doi:10.11992/tis.201801025]
 MO Hongwei,WANG Haibo.Research on human behavior detection based on Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2018,13():967.[doi:10.11992/tis.201801025]
[19]胡越,罗东阳,花奎,等.关于深度学习的综述与讨论[J].智能系统学报,2019,14(1):1.[doi:10.11992/tis.201808019]
 HU Yue,LUO Dongyang,HUA Kui,et al.Overview on deep learning[J].CAAI Transactions on Intelligent Systems,2019,14():1.[doi:10.11992/tis.201808019]
[20]孙必慎,石武祯,姜峰.计算视觉核心问题:自然图像先验建模研究综述[J].智能系统学报,2019,14(1):71.[doi:10.11992/tis.201804019]
 SUN Bishen,SHI Wuzhen,JIANG Feng.Core problem in computer vision: survey of natural image prior models[J].CAAI Transactions on Intelligent Systems,2019,14():71.[doi:10.11992/tis.201804019]

备注/Memo

收稿日期:2020-09-16。
基金项目:江苏省自然科学基金项目(BK20181339)
作者简介:郭磊,硕士研究生,主要研究方向为深度习与医学图像处理;王骏,副教授,博士,主要研究方向为机器学习、模糊系统、医学影像分析。主持国家自然科学基金项目1项,江苏省自然科学基金项目1项。2016年获江苏省高校科研成果自然科学一等奖。获得国家发明专利5项,发表学术论文50余篇;王士同,教授,博士,主要研究方向为模式识别、人工智能。曾获教育部、中船总公司、湖南省等省部级科技进步奖10项。获国务院政府特殊津贴,省部级有突出贡献的中青年专家。发表学术论文百余篇.
通讯作者:王骏.E-mail:wangjun_shu@shu.edu.cn

更新日期/Last Update: 2021-12-25
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134