Journal of Shandong University(Engineering Science) ›› 2018, Vol. 48 ›› Issue (6): 1-7.doi: 10.6040/j.issn.1672-3961.0.2018.205
• Machine Learning & Data Mining • Next Articles
Zhenbing LIU1(),Xusheng FANG1,Huihua YANG1,2,Rushi LAN1
CLC Number:
1 | 张柏雯, 林岚, 吴水才, 等. 深度学习在轻度认知障碍转化与分类中的应用分析[J]. 医疗卫生装备, 2017, 38 (9): 105- 111. |
ZHANG Baiwen , LIN Lan , WU Shuicai , et al. Application of deep learning to mild cognitive impairment conversion and classification[J]. Chinese Medical Equipment Journal, 2017, 38 (9): 105- 111. | |
2 |
ORTIZ A , RRIZ J M , REZ J , et al. LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease[J]. Pattern Recognition Letters, 2013, 34 (14): 1725- 1733.
doi: 10.1016/j.patrec.2013.04.014 |
3 | LIU F , SUK H I , WEE C Y , et al. High-order graph matching based feature selection for Alzheimer's disease identification[J]. Medical Image Computing and Computer-Assisted Intervention, 2013, 16 (2): 311- 318. |
4 |
YANG J , PAN P , SONG W , et al. Voxelwise meta-analysis of gray matter anomalies in Alzheimer's disease and mild cognitive impairment using anatomic likelihood estimation[J]. Journal of the Neurological Sciences, 2012, 316 (1-2): 21- 29.
doi: 10.1016/j.jns.2012.02.010 |
5 | 李昕, 童隆正, 周晓霞, 等. 基于MR图像三维纹理特征的阿尔茨海默病和轻度认知障碍的分类[J]. 中国医学影像技术, 2011, 27 (5): 1047- 1051. |
LI Xin , TONG Longzheng , ZHOU Xiaoxia , et al. Classification of 3D texture feature based on MRI image in discrimination of Alzheimer disease and mild cognitive impairment from normal controls[J]. Chinese Journal of Medical Imaging Technology, 2011, 27 (5): 1047- 1051. | |
6 | 何其佳, 刘振丙, 徐涛, 等. 基于LBP和极限学习机的脑部MR图像分类[J]. 山东大学学报(工学版), 2017, 47 (2): 86- 93. |
HE Qijia , LIU Zhenbing , XU Tao , et al. MR image classification based on LBP and extreme learning machine[J]. Journal of Shandong University (Engineering Science), 2017, 47 (2): 86- 93. | |
7 | LIU Z , XU T , MA C , et al. T-test based Alzheimer's disease diagnosis with multi-feature in MRIs[J]. Multimedia Tools & Applications, 2018, (2): 1- 17. |
8 |
ZHANG D , WANG Y , ZHOU L , et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment[J]. Neuroimage, 2011, 55 (3): 856.
doi: 10.1016/j.neuroimage.2011.01.008 |
9 | ZHU X , SUK H I , WANG L , et al. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis[J]. Medical Image Analysis, 2015, 75 (6): 570- 577. |
10 | KRIZHEVSKY A , HINTON G E , SUTSKEVER I . ImageNet classification with deep convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2012, 25 (2): 2012. |
11 | SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL].[2017-06-20].https://arxiv.org/pdf/1049.1556v6.pdf. |
12 | SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: 2015: 1-9. |
13 | HE K, ZHANG X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: 2016: 770-778. |
14 | LIU F, SHEN C. Learning deep convolutional features for MRI based Alzheimer's disease classification [EB/OL].[2017-04-30]. https://arXiv.org/pdf./:1404.3366v1.pdf. |
15 | SAMAN S, GHASSEM T. Deep AD: Alzheimer's disease classification via deep convolutional neural networks using MRI and FMRI[EB/OL].[2016-03-29]. https://arXiv.org/1603.08631v1.pdf. |
16 | BILLONES C D, DEMETRIA O J, HOSTALLERO D E, et al. DemNet: a convolutional neural network for the detection of alzheimer's disease and mild cognitive impairment[C]//Region 10 Conference(TENCON). Singapore: IEEE, 2016: 3724-3727. |
17 | 吕鸿蒙, 赵地, 迟学斌. 基于增强AlexNet的深度学习的阿尔茨海默病的早期诊断[J]. 计算机科学, 2017, 44 (增刊1): 50- 60. |
LYU Hongmeng , ZHAO Di , CHI Xuebin . Deep learning for early diagnosis of Alzheimer's disease based on intensive AlexNet[J]. Computer Science, 2017, 44 (Suppl.1): 50- 60. | |
18 | LIN M, CHEN Q, YAN S. Network in network[EB/OL]. [2016-01-20]. http://arxiv.org/pdf/1312.-4000v3.pdf. |
19 | MADABHUSHI A , UDUPA J K , SOUZA A . Generalized scale: theory, algorithms, and application to image inhomogeneity correction[J]. Proceedings of SPIE-The International Society for Optical Engineering, 2006, 101 (2): 100- 121. |
20 |
ORTIZ A , MUNILLA J , GÓRRIZ J M , et al. Ensembles of deep learning architectures for the early diagnosis of the Alzheimer's disease[J]. International Journal of Neural Systems, 2016, 26 (07): 1650025.
doi: 10.1142/S0129065716500258 |
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