计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 524-527.
苏庆华1, 付景超1, 谷焓2,3, 张姗姗2,3, 李奕飞2,3, 江方舟2,3, 白翰林1, 赵地2
SU Qing-hua1, FU Jing-chao1, GU Han2,3, ZHANG Shan-shan2,3, LI Yi-fei2,3, JIANG Fang-zhou2,3, BAI Han-lin1, ZHAO Di2
摘要: 在癌症高发的当代,前列腺癌作为男性特有的疾病,其发病率逐年升高。卷积神经网络因其在图像识别领域的强大性能而倍受关注,也非常适用于计算机辅助诊断(Computer Aided Design,CAN)领域。由于神经网络模型中通常包含大量参数,因此训练一个卷积神经网络十分耗时。如何加快神经网络的训练成为了深度学习领域中一个十分重要的问题。为了解决这个问题,一般采用多GPU并行方案。其中,数据同步在GPU性能均衡的情况下表现更佳。因此,文中借鉴已有的基于数据并行算法对前列腺三维卷积网络进行加速。
中图分类号:
[1]曹德宏,柳良仁,魏强,等.前列腺癌的治疗研究进展[J].华西医学,2017(2):277-281. [2]SHIN H C,ORTON M R,COLLINS D J,et al.Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2013,35(8):1930-1943. [3]ESTEVA A,KUPREL B,NOVOA R A,et al.Corrigendum:Dermatologist-level classification of skin cancer with deep neural networks[J].Nature,2017,542(7639):115-118. [4]GULSHAN V,PENG L,CORAM M,et al.Development andValidation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs[J].Jama,2016,316(22):2402. [5]LITJENS G,KOOI T,BEJNORDI B E,et al.A survey on deep learning in medical image analysis[J].Medical Image Analysis,2017,42(9):60-88. [6]周飞燕,金林鹏,董军.卷积神经网络研究综述[J]. 计算机学报,2017,40(6):1229-1251. [7]LÉCUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [8]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc.,2012:1097-1105. [9]HSU K L,GUPTA H V,SOROOSHIAN S.Artificial NeuralNetwork Modeling of the Rainfall-Runoff Process[J].Water Resources Research,1995,31(31):2517-2530. [10]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[M]∥Neurocompu-ting:foundations of research.MIT Press,1988:533-536. [11]BOUVRIE J.Notes on Convolutional Neural Networks[J].Neural Nets,2006. [12]钟联波.GPU与CPU的比较分析[J].技术与市场,2009,16(9):13-14. [13]RAMPASEK L,GOLDENBERG A.TensorFlow:Biology’sGateway to Deep Learning?[J].Cell Systems,2016,2(1):12. [14]ROSSUM G V,DRAKE F L.Python 3 Reference Manual[J].Department of Computer Science,1995,111(254):1-52. [15]刘琦.卷积检测模型的GPU加速研究[D].上海:上海交通大学,2014. [16]张任其,李建华,范磊.分布式环境下卷积神经网络并行策略研究[J].计算机工程与应用,2017,53(8):1-7. [17]LIAO F,LIANG M,LI Z,et al.Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network[J].arXiv:1711.08324,2017. [18]ABADI M,AGARWAL A,BARHAM P,et al.TensorFlow:Large-Scale Machine Learning on Heterogeneous Distributed Systems[J].arXiv:1603.04467,2016. |
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