Single-modal neuroimaging computer aided diagnosis for schizophrenia based on ensemble learning using privileged information_Journal of Biomedical Engineering

Authors：

SHEN Lu , WANG Qianting ,  SHI Jun

Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, P.R.China;

Corresponding author：

SHI Jun, Email: junshi@shu.edu.cn

Keywords：

single-modal neuroimaging; schizophrenia; deep learning; extreme learning machine based auto-encoder; ensemble learning using privileged information

DOI：

10.7507/1001-5515.201905029

Video：

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Abstract Full text Figures/Tables Video References Cited by

Neuroimaging technologies have been applied to the diagnosis of schizophrenia. In order to improve the performance of the single-modal neuroimaging-based computer-aided diagnosis (CAD) for schizophrenia, an ensemble learning algorithm based on learning using privileged information (LUPI) was proposed in this work. Specifically, the extreme learning machine based auto-encoder (ELM-AE) was first adopted to learn new feature representation for the single-modal neuroimaging data. Random project algorithm was then performed on the learned high-dimensional features to generate several new feature subspaces. After that, multiple feature pairs were built among these subspaces to work as source domain and target domain, respectively, which were used to train multiple support vector machine plus (SVM+) classifier. Finally, a strong classifier is learned by combining these SVM+ classifiers for classification. The proposed algorithm was evaluated on a public schizophrenia neuroimaging dataset, including the data of structural magnetic resonance imaging (sMRI) and functional MRI (fMRI). The results showed that the proposed algorithm achieved the best diagnosis performance. In particular, the classification accuracy, sensitivity and specificity of the proposed algorithm were 72.12% ± 8.20%, 73.50% ± 15.44% and 70.93% ± 12.93%, respectively, on the sMRI data, and it also achieved the classification accuracy of 72.33% ± 8.95%, sensitivity of 68.50% ± 16.58% and specificity of 75.73% ± 16.10% on the fMRI data. The proposed algorithm overcomes the problem that the traditional LUPI methods need the additional privileged information modality as source domain. It can be directly applied to the single-modal data for classification, and also can improve the classification performance. Therefore, it suggests that the proposed algorithm will have wider applications.

Citation： SHEN Lu, WANG Qianting, SHI Jun. Single-modal neuroimaging computer aided diagnosis for schizophrenia based on ensemble learning using privileged information. Journal of Biomedical Engineering, 2020, 37(3): 405-411, 418. doi: 10.7507/1001-5515.201905029 Copy

1.	管丽丽, 杜立哲, 马弘. 精神分裂症的疾病负担. 中国心理卫生杂志, 2012, 26(12): 913-919.
2.	Birur B, Kraguljac N V, Shelton R C, et al. Brain structure, function, and neurochemistry in schizophrenia and bipolar disorder-a systematic review of the magnetic resonance neuroimaging literature. NPJ Schizophrenia, 2017, 3(1): 15.
3.	Shi Jun, Zheng Xiao, Li Yan, et al. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease. IEEE J Biomed Health Inform, 2018, 22(1): 173-183.
4.	Shi Jun, Xue Zeyu, Dai Yakang, et al. Cascaded multi-column RVFL+ classifier for single-modal neuroimaging-based diagnosis of Parkinson’s disease. IEEE Trans Biomed Eng, 2019, 66(8): 2362-2371.
5.	Kasun L L C, Zhou H, Huang G B, et al. Representational learning with extreme learning machine for big data. IEEE Intell Syst, 2013, 28(6): 31-34.
6.	Tang Jiexiong, Deng Chenwei, Huang Guangbin. Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst, 2016, 27(4): 809-821.
7.	Zhang Junjie, Yin Jie, Zhang Qi, et al. Robust sound event classification with bilinear multi-column ELM-AE and two-stage ensemble learning. EURASIP Journal on Audio, Speech, and Music Processing, 2017: 11.
8.	Vapnik V, Vashist A. A new learning paradigm: learning using privileged information. Neural Netw, 2009, 22(5/6): 544-557.
9.	Duan Lixin, Xu Yanwu, Li Wen, et al. Incorporating privileged genetic information for fundus image based glaucoma detection// International Conference on Medical Image Computing and Computer-Assisted Intervention. Boston: Springer, 2014: 204-211.
10.	Zheng Xiao, Shi Jun, Ying Shihui, et al. Improving single-modal neuroimaging based diagnosis of brain disorders via boosted privileged information learning framework// International Workshop on Machine Learning in Medical Imaging. Athens: Springer, 2016: 95-103.
11.	Zheng X, Shi J, Zhang Q, et al. Improving MRI-based diagnosis of Alzheimer’s disease via an ensemble privileged information learning algorithm// 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne: IEEE, 2017: 456-459.
12.	Huang Guangbin, Zhu Qinyu, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1/3): 489-501.
13.	Zhu P F, Zhang L, Hu Q H, et al. Multi-scale patch based collaborative representation for face recognition with margin distribution optimization// European Conference on Computer Vision. Firenze: Springer, 2012: 822-835.
14.	Yang Fan, Lu Huchuan, Yang M H. Robust visual tracking via multiple kernel boosting with affinity constraints. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(2): 242-254.
15.	Xu Lai, Groth K M, Pearlson G, et al. Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia. Hum Brain Mapp, 2009, 30(3): 711-724.
16.	Jafri M J, Pearlson G D, Stevens M, et al. A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage, 2008, 39(4): 1666-1681.
17.	Esposito F, Scarabino T, Hyvarinen A, et al. Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage, 2005, 25(1): 193-205.
18.	Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural Netw, 2000, 13(4/5): 411-430.
19.	Calhoun V D, Adali T, Pearlson G D, et al. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp, 2001, 14(3): 140-151.
20.	Silva R F, Castro E, Gupta C N, et al. The tenth annual MLSP competition: Schizophrenia classification challenge// 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Reims: IEEE, 2014: 1-6.

1. 管丽丽, 杜立哲, 马弘. 精神分裂症的疾病负担. 中国心理卫生杂志, 2012, 26(12): 913-919.
2. Birur B, Kraguljac N V, Shelton R C, et al. Brain structure, function, and neurochemistry in schizophrenia and bipolar disorder-a systematic review of the magnetic resonance neuroimaging literature. NPJ Schizophrenia, 2017, 3(1): 15.
3. Shi Jun, Zheng Xiao, Li Yan, et al. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease. IEEE J Biomed Health Inform, 2018, 22(1): 173-183.
4. Shi Jun, Xue Zeyu, Dai Yakang, et al. Cascaded multi-column RVFL+ classifier for single-modal neuroimaging-based diagnosis of Parkinson’s disease. IEEE Trans Biomed Eng, 2019, 66(8): 2362-2371.
5. Kasun L L C, Zhou H, Huang G B, et al. Representational learning with extreme learning machine for big data. IEEE Intell Syst, 2013, 28(6): 31-34.
6. Tang Jiexiong, Deng Chenwei, Huang Guangbin. Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst, 2016, 27(4): 809-821.
7. Zhang Junjie, Yin Jie, Zhang Qi, et al. Robust sound event classification with bilinear multi-column ELM-AE and two-stage ensemble learning. EURASIP Journal on Audio, Speech, and Music Processing, 2017: 11.
8. Vapnik V, Vashist A. A new learning paradigm: learning using privileged information. Neural Netw, 2009, 22(5/6): 544-557.
9. Duan Lixin, Xu Yanwu, Li Wen, et al. Incorporating privileged genetic information for fundus image based glaucoma detection// International Conference on Medical Image Computing and Computer-Assisted Intervention. Boston: Springer, 2014: 204-211.
10. Zheng Xiao, Shi Jun, Ying Shihui, et al. Improving single-modal neuroimaging based diagnosis of brain disorders via boosted privileged information learning framework// International Workshop on Machine Learning in Medical Imaging. Athens: Springer, 2016: 95-103.
11. Zheng X, Shi J, Zhang Q, et al. Improving MRI-based diagnosis of Alzheimer’s disease via an ensemble privileged information learning algorithm// 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne: IEEE, 2017: 456-459.
12. Huang Guangbin, Zhu Qinyu, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1/3): 489-501.
13. Zhu P F, Zhang L, Hu Q H, et al. Multi-scale patch based collaborative representation for face recognition with margin distribution optimization// European Conference on Computer Vision. Firenze: Springer, 2012: 822-835.
14. Yang Fan, Lu Huchuan, Yang M H. Robust visual tracking via multiple kernel boosting with affinity constraints. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(2): 242-254.
15. Xu Lai, Groth K M, Pearlson G, et al. Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia. Hum Brain Mapp, 2009, 30(3): 711-724.
16. Jafri M J, Pearlson G D, Stevens M, et al. A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage, 2008, 39(4): 1666-1681.
17. Esposito F, Scarabino T, Hyvarinen A, et al. Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage, 2005, 25(1): 193-205.
18. Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural Netw, 2000, 13(4/5): 411-430.
19. Calhoun V D, Adali T, Pearlson G D, et al. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp, 2001, 14(3): 140-151.
20. Silva R F, Castro E, Gupta C N, et al. The tenth annual MLSP competition: Schizophrenia classification challenge// 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Reims: IEEE, 2014: 1-6.

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