计算机与控制工程 |
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阿尔茨海默病辅助诊断的多模态数据融合轻量级网络 |
王光明( ),柏正尧*( ),宋帅,徐月娥 |
云南大学 信息学院,云南 昆明 650504 |
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Lightweight multimodal data fusion network for auxiliary diagnosis of Alzheimer’s disease |
Guangming WANG( ),Zhengyao BAI*( ),Shuai SONG,Yue’e XU |
School of Information Science and Engineering, Yunnan University, Kunming 650504, China |
引用本文:
王光明,柏正尧,宋帅,徐月娥. 阿尔茨海默病辅助诊断的多模态数据融合轻量级网络[J]. 浙江大学学报(工学版), 2025, 59(1): 39-48.
Guangming WANG,Zhengyao BAI,Shuai SONG,Yue’e XU. Lightweight multimodal data fusion network for auxiliary diagnosis of Alzheimer’s disease. Journal of ZheJiang University (Engineering Science), 2025, 59(1): 39-48.
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https://www.zjujournals.com/eng/CN/Y2025/V59/I1/39
|
36 |
LI Changjun, TIAN Miaoyuan, LI Qiwei, et al Action mechanism of acupuncture intervention on hippocampus of Alzheimer’s disease[J]. Journal of Clinical Acupuncture and Moxibustion, 2023, 39 (7): 102- 106
|
37 |
SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization [C]// Proceedings of the IEEE International Conference on Computer Vision . Venice: IEEE, 2017: 618–626.
|
1 |
YAO Z, WANG H, YAN W, et al Artificial intelligence-based diagnosis of Alzheimer’s disease with brain MRI images[J]. European Journal of Radiology, 2023, 165: 110934
doi: 10.1016/j.ejrad.2023.110934
|
2 |
MENG Q, LIN M S, TZENG I S Relationship between exercise and Alzheimer’s disease: a narrative literature review[J]. Frontiers in Neuroscience, 2020, 14: 131
doi: 10.3389/fnins.2020.00131
|
3 |
GAUTHIER S, ISMAIL Z, GOODARZI Z, et al Clinicians’ perspectives on how disease modifying drugs for Alzheimer’s disease impact specialty care[J]. The Journal of Prevention of Alzheimer’s Disease, 2023, 10: 339- 341
|
4 |
REN R, QI J, LIN S, et al The China Alzheimer report 2022[J]. Gen Psychiatr, 2022, 35 (1): e100751
doi: 10.1136/gpsych-2022-100751
|
5 |
LIU S, SONG Y, CAI W, et al. Multifold Bayesian kernelization in Alzheimer’s diagnosis [J] Medical Image Computing and Computer-Assisted Intervention , 2013, 16(3): 303–310.
|
6 |
ZHANG D, WANG Y, ZHOU L, et al Multimodal classification of Alzheimer’s disease and mild cognitive impairment[J]. Neuroimage, 2011, 55 (3): 856- 867
doi: 10.1016/j.neuroimage.2011.01.008
|
7 |
LIN W, GAO Q, DU M, et al Multiclass diagnosis of stages of Alzheimer’s disease using linear discriminant analysis scoring for multimodal data[J]. Computers in Biology and Medicine, 2021, 134: 104478
doi: 10.1016/j.compbiomed.2021.104478
|
8 |
FENG W, HALM-LUTTERODT N V, TANG H, et al Automated MRI-based deep learning model for detection of Alzheimer’s disease process[J]. International Journal of Neural Systems, 2020, 30 (6): 2050032
doi: 10.1142/S012906572050032X
|
9 |
LIU M, CHENG D, YAN W Classification of Alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images[J]. Frontiers in Neuroinformatics, 2018, 12: 35
doi: 10.3389/fninf.2018.00035
|
10 |
KONG Z, ZHANG M, ZHU W, et al Multi-modal data Alzheimer’s disease detection based on 3D convolution[J]. Biomedical Signal Processing and Control, 2022, 75: 103565
doi: 10.1016/j.bspc.2022.103565
|
11 |
韩坤, 潘海为, 张伟, 等 基于多模态医学图像的Alzheimer病分类方法[J]. 清华大学学报: 自然科学版, 2020, 60 (8): 664- 671 HAN Kun, PAN Haiwei, ZHANG Wei, et al Alzheimer’s disease classification method based on multimodal medical imaging[J]. Journal of Tsinghua University: Science and Technology, 2020, 60 (8): 664- 671
|
12 |
LIANG C, LAO H, WEI T, et al Alzheimer’s disease classification from hippocampal atrophy based on PCANet-BLS[J]. Multimedia Tools and Applications, 2022, 81: 11187- 11203
doi: 10.1007/s11042-022-12228-0
|
13 |
DEEPA N, CHOKKALINGAM S P Optimization of VGG16 utilizing the arithmetic optimization algorithm for early detection of Alzheimer’s disease[J]. Biomedical Signal Processing and Control, 2022, 74: 103455
doi: 10.1016/j.bspc.2021.103455
|
14 |
张默文. 基于结构磁共振成像和神经网络的阿尔兹海默症诊断方法研究[D]. 济南: 山东师范大学, 2023:1–60. ZHANG Mowen. Diagnosis of Alzheimer’s disease based on structural magnetic resonance imaging and neural network [D]. Jinan: Shandong Normal University, 2023: 1–60.
|
15 |
MEHTA S C, RASTEGARI M. MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer [EB/OL]. (2022−03−04)[2024−04−16], https://arxiv.org/pdf/2110.02178.
|
16 |
JACK C R JR, BERNSTEIN M A, FOX N C, et al The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods[J]. Journal of Magnetic Resonance Imaging, 2008, 27 (4): 685- 691
doi: 10.1002/jmri.21049
|
17 |
JENKINSON M, BECKMANN C F, BEHRENS T E J, et al FSL[J]. Neuroimage, 2012, 62 (2): 782- 790
doi: 10.1016/j.neuroimage.2011.09.015
|
18 |
XU Z, DENG H, LIU J, et al Diagnosis of Alzheimer’s disease based on the modified Tresnet[J]. Electronics, 2021, 10 (16): 1908
doi: 10.3390/electronics10161908
|
19 |
ODUSAMI M, MASKELIŪNAS R, DAMAŠEVIČIUS R Pixel-level fusion approach with vision transformer for early detection of Alzheimer’s disease[J]. Electronics, 2023, 12 (5): 1218
doi: 10.3390/electronics12051218
|
20 |
陈蕊森, 夏军, 陈建良, 等 基于VBM的AD患者整体认知水平与脑灰质容积关系的横断面研究[J]. CT理论与应用研究, 2018, 27 (6): 709- 717 CHEN Ruisen, XIA Jun, CHEN Jianliang, et al Cross-sectional study on the relationship between integral cognitive level and gray matter volume in patients with Alzheimer’s disease based on VBM[J]. CT Theory and Applications, 2018, 27 (6): 709- 717
|
21 |
SANDLER M, HOWARD A, ZHU M, et al. MobileNetV2: inverted residuals and linear bottlenecks [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City: IEEE, 2018: 4510–4520.
|
22 |
RUSSAKOVSKY O, DENG J, SU H, et al ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115 (3): 211- 252
doi: 10.1007/s11263-015-0816-y
|
23 |
LI J, WEN Y, HE L. SCConv: spatial and channel reconstruction convolution for feature redundancy [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver: IEEE, 2023: 6153–6162.
|
24 |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems . Long Beach: [s.n.], 2017: 6000–6010.
|
25 |
HUANG Z, ZHANG Z, LAN C, et al. Adaptive frequency filters as efficient global token mixers [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision . Paris: IEEE, 2023: 6049–6059.
|
26 |
DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: transformers for image recognition at scale [EB/OL]. (2021−06−03)[2024−04−16]. https://arxiv.org/pdf/2010.11929.
|
27 |
CHOLLET F. Xception: deep learning with depth wise separable convolutions [C]// IEEE Conference on Computer Vision and Pattern Recognition . Honolulu: IEEE, 2017: 1800−1807.
|
28 |
LENG Z Q, TAN M, LIU C, et al. PloyLoss: a polynomial expansion perspective of classification loss functions [EB/OL]. (2022−05−10)[2024−04−16]. https://arxiv.org/pdf/2204.12511.
|
29 |
HERINGTON J, MCCRADDEN M D, CREEL K, et al Ethical considerations for artificial intelligence in medical imaging: data collection, development, and evaluation[J]. Journal of Nuclear Medicine, 2023, 64 (12): 1848- 1854
doi: 10.2967/jnumed.123.266080
|
30 |
ZHU X, SUK H, SHEN D A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis[J]. Neuroimage, 2014, 100: 91- 105
doi: 10.1016/j.neuroimage.2014.05.078
|
31 |
HAO X, BAO Y, GUO Y, et al Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer’s disease[J]. Medical Image Analysis, 2020, 60: 101625
doi: 10.1016/j.media.2019.101625
|
32 |
ZHU Q, YUAN N, HUANG J, et al Multi-modal AD classification via self-paced latent correlation analysis[J]. Neurocomputing, 2019, 355: 143- 154
doi: 10.1016/j.neucom.2019.04.066
|
33 |
TONG T, GRAY K, GAO Q, et al Multi-modal classification of Alzheimer’s disease using nonlinear graph fusion[J]. Pattern Recognition, 2017, 63: 171- 181
doi: 10.1016/j.patcog.2016.10.009
|
34 |
ZHANG F, LI Z, ZHANG B, et al Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease[J]. Neurocomputing, 2019, 361: 185- 195
doi: 10.1016/j.neucom.2019.04.093
|
35 |
李伟汉, 侯北平, 胡飞阳, 等 阿尔茨海默症的多模态分类方法[J]. 应用科学学报, 2023, 41 (6): 1004- 1018 LI Weihan, HOU Beiping, HU Feiyang, et al Multi-modal diagnosis method for Alzheimer’s disease[J]. Journal of Applied Sciences, 2023, 41 (6): 1004- 1018
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