计算机科学 ›› 2020, Vol. 47 ›› Issue (9): 185-189.doi: 10.11896/jsjkx.190900001
潘祖江1, 刘宁1, 张伟2, 王建勇1
PAN Zu-jiang1, LIU Ning1, ZHANG Wei2, WANG Jian-yong1
摘要: 阿尔茨海默症是一种不可逆的神经退化疾病,由于脑组织的退化而产生的严重的认知问题。目前已有许多临床实验和研究计划来研究阿尔茨海默症的病理学,这些实验和计划会产生一些可以用来分析的数据。文中着重结合多种临床特征,对阿尔茨海默症进行自动诊断,并预测潜在的预后风险,进而提出了一个基于层次注意力机制的多任务疾病进展模型。该模型将疾病自动诊断任务作为主任务,疾病预后预测任务作为辅任务,以提升模型的泛化能力,进而提升疾病自动诊断任务的效果。其应用了两层的注意力机制,注意力分别应用在特征层和就诊记录层,使得模型可以对不同的特征以及不同的就诊记录有不同的注意力。在ADNI(Alzheimer’s Disease Neuroimaging Initiative)数据集上进行实验,并将所提模型与多个基准模型进行比较,实验结果表明,提出的模型具有更好的效果,为临床实际应用提供了更好的鲁棒性。
中图分类号:
[1] PATTERSON C.World Alzheimer Report 2018—The state of the art of dementia research:New frontiers[R].Alzheimer’s Disease International (ADI):London,UK,2018. [2] WANG Q,SUN M,ZHAN L,et al.Multi-Modality DiseaseModeling via Collective Deep Matrix Factorization[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2017:1155-1164. [3] DAI P,GWADRY-SRIDHAR F,BAUER M,et al.Healthy cognitive aging:A hybrid random vector functional-link model for the analysis of alzheimer’s disease[C]//Thirty-First AAAI Conference on Artificial Intelligence.2017. [4] HUANG G B,ZHU Q Y,SIEW C K.Extreme learning ma-chine:theory and applications[J].Neurocomputing,2006,70(1/2/3):489-501. [5] HINTON G,DENG L,YU D,et al.Deep neural networks for acoustic modeling in speech recognition[J].IEEE Signal proces-sing magazine,2012,29(6):82-97. [6] LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436. [7] TUFAIL A B,ABIDI A,SIDDIQUI A M,et al.Automatic classification of initial categories of Alzheimer’s disease from structural MRI phase images:a comparison of PSVM,KNN and ANN methods[J].Age,2012,2012:1731. [8] LEBEDEV A V,WESTMAN E,VAN WESTEN G J P,et al.Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness[J].NeuroImage:Clinical,2014,6:115-125. [9] LÓPEZ M,RAMÍREZ J,GÓRRIZ J M,et al.Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer's disease[J].Neurocomputing,2011,74(8):1260-1271. [10] SHI B,CHEN Y,HOBBS K,et al.Nonlinear Metric Learning for Alzheimer’s Disease Diagnosis with Integration of Longitudinal Neuroimaging Features[C]//BMVC.2015. [11] DAI P,GWADRY-SRIDHAR F,BAUER M,et al.Bagging ensembles for the diagnosis and prognostication of alzheimer’s di-sease[C]//Thirtieth AAAI Conference on Artificial Intelligence.2016. [12] XING E P,JORDAN M I,RUSSELL S J,et al.Distance metric learning with application to clustering with side-information[C]//Advances in neural information processing systems.2003:521-528. [13] LIN T,ZHA H.Riemannian manifold learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(5):796-809. [14] 邱锡鹏.神经网络与深度学习[OL].[2017-04-21].https://nndl.github.io/. [15] BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv:1409.0473,2014. [16] YANG Z,YANG D,DYER C,et al.Hierarchical attention networks for document classification[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2016:1480-1489. [17] MNIH V,HEESS N,GRAVES A.Recurrent models of visualattention[C]//Advances in Neural Information Processing Systems.2014:2204-2212. [18] YANG Y,YANG L,ZOU Y B,et al.Humor Recognition Based on Linguistic Features and Hierarchical Attention Mechanism[J].Computer Engineering,2020,46(8):64-71. [19] CHOI E,BAHADORI M T,SUN J,et al.Retain:An interpretable predictive model for healthcare using reverse time attention mechanism[C]//Advances in Neural Information Processing Systems.2016:3504-3512. [20] COLLOBERT R,WESTON J.A unified architecture for natural language processing:Deep neural networks with multitask lear-ning[C]//Proceedings of the 25th International Conference on Machine Learning.ACM,2008:160-167. [21] ZHANG W J.An Online Multi-Task Learning Algorithm Based on Weight Matrix Decomposition[J].Computer Engineering,2019,45(8):190-197. [22] DENG L,HINTON G,KINGSBURY B.New types of deep neural network learning for speech recognition and related applications:An overview[C]//2013 IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2013:8599-8603. [23] GIRSHICK R.Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1440-1448. [24] RAMSUNDAR B,KEARNES S,RILEY P,et al.Massivelymultitask networks for drug discovery[J].arXiv:1502.02072,2015. [25] RUDER S.An overview of multi-task learning in deep neuralnetworks[J].arXiv:1706.05098,2017. [26] CARUANA R.Multitask learning[J].Machine Learning,1997,28(1):41-75. [27] BAXTER J.A Bayesian/information theoretic model of learning to learn via multiple task sampling[J].Machine Learning,1997,28(1):7-39. [28] HOCHREITER S,SCHMIDHUBER J.Long short-term memo-ry[J].Neural Computation,1997,9(8):1735-1780. [29] KINGMA D P,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014. [30] WEINER M W,VEITCH D P,AISEN P S,et al.The Alzhemer’sDisease Neuroimaging Initiative:a review of papers published since its inception[J].Alzheimer’s & Dementia,2013,9(5):e111-e194. |
[1] | 周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085 |
[2] | 戴禹, 许林峰. 基于文本行匹配的跨图文本阅读方法 Cross-image Text Reading Method Based on Text Line Matching 计算机科学, 2022, 49(9): 139-145. https://doi.org/10.11896/jsjkx.220600032 |
[3] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
[4] | 熊丽琴, 曹雷, 赖俊, 陈希亮. 基于值分解的多智能体深度强化学习综述 Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization 计算机科学, 2022, 49(9): 172-182. https://doi.org/10.11896/jsjkx.210800112 |
[5] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[6] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
[7] | 汪鸣, 彭舰, 黄飞虎. 基于多时间尺度时空图网络的交通流量预测模型 Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction 计算机科学, 2022, 49(8): 40-48. https://doi.org/10.11896/jsjkx.220100188 |
[8] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
[9] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[10] | 闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042 |
[11] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
[12] | 熊罗庚, 郑尚, 邹海涛, 于化龙, 高尚. 融合双向门控循环单元和注意力机制的软件自承认技术债识别方法 Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism 计算机科学, 2022, 49(7): 212-219. https://doi.org/10.11896/jsjkx.210500075 |
[13] | 彭双, 伍江江, 陈浩, 杜春, 李军. 基于注意力神经网络的对地观测卫星星上自主任务规划方法 Satellite Onboard Observation Task Planning Based on Attention Neural Network 计算机科学, 2022, 49(7): 242-247. https://doi.org/10.11896/jsjkx.210500093 |
[14] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
[15] | 曾志贤, 曹建军, 翁年凤, 蒋国权, 徐滨. 基于注意力机制的细粒度语义关联视频-文本跨模态实体分辨 Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism 计算机科学, 2022, 49(7): 106-112. https://doi.org/10.11896/jsjkx.210500224 |
|