前列腺癌辅助诊断GPU并行算法设计

计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 524-527.

前列腺癌辅助诊断GPU并行算法设计

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

(北京物资学院信息学院北京101149)¹;
(中国科学院计算技术研究所北京100190)²;
(北京邮电大学北京100089)³

出版日期:2019-11-10 发布日期:2019-11-20
通讯作者: 赵地(1978－),男,博士,副教授,CCF会员,主要研究方向为类脑计算、深度学习,E-mail:zhaodi@escience.cn。
作者简介:苏庆华(1980－),女,博士,副教授,主要研究方向为医学影像处理,E-mail:qinghuasu@126.com。
基金资助:
本文受国家自然科学基金(61803035)资助。

Parallel Algorithm Design for Assisted Diagnosis of Prostate Cancer

SU Qing-hua¹, FU Jing-chao¹, GU Han^2,3, ZHANG Shan-shan^2,3, LI Yi-fei^2,3, JIANG Fang-zhou^2,3, BAI Han-lin¹, ZHAO Di²

(Information Engineering School,Beijing Wuzi University,Beijing 101149,China)¹;
(Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)²;
(Beijing University of Posts and Telecommunications,Beijing 100089,China)³

Online:2019-11-10 Published:2019-11-20

摘要/Abstract

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。

关键词: 多GPU并行, 卷积网络, 深度学习, 神经网络, 数据并行

Abstract: In the contemporary era of high cancer,prostate cancer is a unique disease for men,and the incidence is increasing year by year.Convolutional neural networks have attracted much attention due to their powerful performance in the field of image recognition,and are also very suitable for computer-aided diagnosis.Training a convolutional neural network is time consuming because neural network models often contain a large number of parameters.How to accele-rate the training of neural networks has become a very important issue in the field of deep learning.To solve this problem,a multi-GPU parallel scheme is generally adopted.Among them,data synchronization performs better when the GPU performance is balanced.Therefore,this paper draws on the algorithm based on data parallel to accelerate the three-dimensional convolution network of prostate.

Key words: Convolutional network, Data parallelism, Deep learning, Multi-GPU parallelism, Neural network

中图分类号:

G312

苏庆华, 付景超, 谷焓, 张姗姗, 李奕飞, 江方舟, 白翰林, 赵地. 前列腺癌辅助诊断GPU并行算法设计[J]. 计算机科学, 2019, 46(11A): 524-527. https://doi.org/

SU Qing-hua, FU Jing-chao, GU Han, ZHANG Shan-shan, LI Yi-fei, JIANG Fang-zhou, BAI Han-lin, ZHAO Di. Parallel Algorithm Design for Assisted Diagnosis of Prostate Cancer[J]. Computer Science, 2019, 46(11A): 524-527. https://doi.org/

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