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Volume 10 Issue 7
Jul.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(7): 1628-1630
L. Chen and X. Luo, “Tensor distribution regression based on the 3D conventional neural networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1628–1630, Jul. 2023. doi: 10.1109/JAS.2023.123591
Citation: L. Chen and X. Luo, “Tensor distribution regression based on the 3D conventional neural networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1628–1630, Jul. 2023. doi: 10.1109/JAS.2023.123591
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Tensor Distribution Regression Based on the 3D Conventional Neural Networks

doi: 10.1109/JAS.2023.123591
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    • References(18)
  • [1]
    B. Zhang and Z. Zhu, “Linearized proximal alternating direction method of multipliers for parallel magnetic resonance imaging,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 763–769, 2017. doi: 10.1109/JAS.2016.7510226
    [2]
    M. Liu, J. Zhang, E. Adeli, et al., “Joint classification and regression via deep multi-task multi-channel learning for Alzheimer’s disease diagnosis,” IEEE Trans. Biomedical Engineering, vol. 66, no. 5, pp. 1195–1206, May 2019. doi: 10.1109/TBME.2018.2869989
    [3]
    J. Cheng, Z. Liu, H. Guan, et al., “Brain age estimation from MRI using cascade networks with ranking loss,” IEEE Trans. Medical Imaging, vol. 40, no. 12, pp. 3400–3412, Dec. 2021. doi: 10.1109/TMI.2021.3085948
    [4]
    J. H. Morra, Z. Tu, L. G. Apostolova, et al., “Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls,” Neuroimage, vol. 45, no. 1, pp. S3–S15, 2009. doi: 10.1016/j.neuroimage.2008.10.043
    [5]
    B. Lei, M. Yang, P. Yang, et al., “Deep and joint learning of longitudinal data for Alzheimer’s disease prediction,” Pattern Recognition, vol. 102, p. 107247, 2020. doi: 10.1016/j.patcog.2020.107247
    [6]
    M. Liu, J. Zhang, et al., “Weakly supervised deep learning for brain disease prognosis using MRI and incomplete clinical scores,” IEEE Trans. Cybernetics, vol. 50, no. 7, pp. 3381–3392, Jul. 2020.
    [7]
    C. Lian, M. Liu, J. Zhang, et al., “Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 42, no. 4, pp. 880–893, Apr. 2020. doi: 10.1109/TPAMI.2018.2889096
    [8]
    B. Lei, P. Yang, T. Wang, et al., “Relational-regularized discriminative sparse learning for Alzheimer’s disease diagnosis,” IEEE Trans. Cybernetics, vol. 47, no. 4, pp. 1102–1113, Apr. 2017. doi: 10.1109/TCYB.2016.2644718
    [9]
    J. Zhang, Y. Gao, Y. Gao, et al., “Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis,” IEEE Trans. Medical Imaging, vol. 35, no. 12, pp. 2524–2533, Dec. 2016. doi: 10.1109/TMI.2016.2582386
    [10]
    W. Zhu, L. Sun, et al., “Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI,” IEEE Trans. Medical Imaging, vol. 40, no. 9, pp. 2354–2366, Sept. 2021. doi: 10.1109/TMI.2021.3077079
    [11]
    H. Peng, W. Gong, C. F. Beckmann, et al., “Accurate brain age prediction with lightweight deep neural networks,” Medical Image Analysis, vol. 68, p. 101871, 2021. doi: 10.1016/j.media.2020.101871
    [12]
    M. Jenkinson, C. F. Beckmann, T. E. Behrens, et al., “FSl,” Neuroimage, vol. 62, no. 2, pp. 782–790, 2012. doi: 10.1016/j.neuroimage.2011.09.015
    [13]
    Y. Lei, H. Zhu, J. Zhang, et al., “Meta ordinal regression forest for medical image classification with ordinal labels,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1–15, Jul. 2022. doi: 10.1109/JAS.2022.105719
    [14]
    X. Luo, H. Wu, Z. Wang, et al., “A novel approach to large-scale dynamically weighted directed network representation,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 44, no. 12, pp. 9756–9773, Dec. 2022. doi: 10.1109/TPAMI.2021.3132503
    [15]
    C. Leng, H. Zhang, et al., “Total variation constrained non-negative matrix factorization for medical image registration,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1025–1037, May 2021. doi: 10.1109/JAS.2021.1003979
    [16]
    J. Kossaifi, Z. C. Lipton, A. Kolbeinsson, et al., “Tensor regression networks,” J. Machine Learning Research, vol. 21, no. 1, pp. 4862–4882, 2020.
    [17]
    B.-B. Gao, C. Xing, C.-W. Xie, et al., “Deep label distribution learning with label ambiguity,” IEEE Trans. Image Processing, vol. 26, no. 6, pp. 2825–2838, Jun. 2017. doi: 10.1109/TIP.2017.2689998
    [18]
    H. Qiao, L. Chen, and F. Zhu, “Ranking convolutional neural network for Alzheimer’s disease mini-mental state examination prediction at multiple time-points,” Computer Methods Programs Biomedicine, vol. 213, p. 106503, 2022. doi: 10.1016/j.cmpb.2021.106503
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