skip to main content
research-article

Adaptive Multi-Task Dual-Structured Learning with Its Application on Alzheimer’s Disease Study

Published: 24 May 2021 Publication History

Abstract

Multi-task learning has been widely applied to Alzheimer’s Disease (AD) studies due to its capability of simultaneously rating the disease severity (classification) and predicting corresponding clinical scores (regression). In this article, we propose a novel technique of Adaptive Multi-task Dual-Structured Learning, named AMDSL, by mutually exploring the dual manifold structure for the label and regression score of the disease data under joint classification and regression tasks, while learning an adaptive shared similarity measure and corresponding feature mapping among these two tasks. We encode both the reconstructed label representation and regression score adaptive to the ideal similarity measure on disease data to achieve the ideal performance on these two joint tasks. The alternating algorithm is proposed to optimize the above objective. We theoretically prove the convergence of the optimization algorithm. The superiority of AMDSL is experimentally validated under joint classification and regression as per various evaluation metrics against the most authoritative Alzheimer’s disease data.

References

[1]
Alzheimer’s Association. 2018. 2018 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia 14 (2018), 367–429.
[2]
Lodewijk Brand, Hua Wang, Heng Huang, Shannon L. Risacher, Andrew J. Saykin, and Li Shen. [n.d.]. Joint high-order multi-task feature learning to predict the progression of Alzheimer’s disease. In Medical Image Computing and Computer Assisted Intervention, 21st International Conference, Proceedings, Part I (MICCAI ’18) (Lecture Notes in Computer Science), Vol. 11070. 555–562.
[3]
Peng Cao, Xuanfeng Shan, Dazhe Zhao, Min Huang, and Osmar R. Zaïane. 2017. Sparse shared structure based multi-task learning for MRI based cognitive performance prediction of Alzheimer’s disease. Pattern Recognit. 72 (2017), 219–235.
[4]
Young-Sang Cho, Joon-Kyung Seong, Yong Jeong, and Sung Yong Shin. 2012. Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. NeuroImage 59, 3 (2012), 2217–2230.
[5]
Xin Geng. 2016. Label distribution learning. IEEE Trans. Knowl. Data Eng. 28, 7 (2016), 1734–1748.
[6]
Peng Hou, Xin Geng, and Min-Ling Zhang. 2016. Multi-label manifold learning. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. AAAI Press, 1680–1686.
[7]
Pengbo Jiang, Xuetong Wang, Qiongling Li, Leiming Jin, and Shuyu Li. 2019. Correlation-aware sparse and low-rank constrained multi-task learning for longitudinal analysis of Alzheimer’s disease. IEEE J. Biomed. Health Informatics 23, 4 (2019), 1450–1456.
[8]
Biao Jie, Mingxia Liu, Jun Liu, Daoqiang Zhang, and Dinggang Shen. 2017. Temporally constrained group sparse learning for longitudinal data analysis in Alzheimer’s disease. IEEE Trans. Biomed. Eng. 64, 1 (2017), 238–249.
[9]
Feng Liu, Chong-Yaw Wee, Huafu Chen, and Dinggang Shen. 2014. Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s Disease and mild cognitive impairment identification. NeuroImage 84 (2014), 466–475.
[10]
Mingxia Liu, Jun Zhang, Ehsan Adeli, and Dinggang Shen. 2017. Deep multi-task multi-channel learning for joint classification and regression of brain status. In Medical Image Computing and Computer Assisted Intervention, 20th International Conference, Proceedings, Part III (MICCAI ’ 17) (Lecture Notes in Computer Science), Vol. 10435. Springer, 3–11.
[11]
Mingxia Liu, Jun Zhang, Ehsan Adeli, and Dinggang Shen. 2019. Joint classification and regression via deep multi-task multi-channel learning for Alzheimer’s disease diagnosis. IEEE Trans. Biomed. Eng. 66, 5 (2019), 1195–1206.
[12]
Feiping Nie, Heng Huang, Xiao Cai, and Chris H. Q. Ding. 2010. Efficient and robust feature selection via joint -norms minimization. In Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, Proceedings. Curran Associates, Inc., 1813–1821.
[13]
Liqiang Nie, Luming Zhang, Lei Meng, Xuemeng Song, Xiaojun Chang, and Xuelong Li. 2017. Modeling disease progression via multisource multitask learners: A case study with Alzheimer’s disease. IEEE Trans. Neural Networks Learn. Syst. 28, 7 (2017), 1508–1519.
[14]
Tianji Pang, Feiping Nie, Junwei Han, and Xuelong Li. 2019. Efficient feature selection via norm constrained sparse regression. IEEE Trans. Knowl. Data Eng. 31, 5 (2019), 880–893.
[15]
Hanyang Peng and Yong Fan. 2017. A general framework for sparsity regularized feature selection via iteratively reweighted least square minimization. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI ’17). AAAI Press, 2471–2477.
[16]
Hanyang Peng and Yong Fan. 2017. A general framework for sparsity regularized feature selection via iteratively reweighted least square minimization. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI Press, 2471–2477.
[17]
Mert R. Sabuncu and Ender Konukoglu. 2015. Clinical prediction from structural brain MRI scans: A large-scale empirical study. Neuroinformatics 13, 1 (2015), 31–46.
[18]
Matt Silver, Eva Janousová, Xue Hua, Paul M. Thompson, and Giovanni Montana. 2012. Identification of gene pathways implicated in Alzheimer’s disease using longitudinal imaging phenotypes with sparse regression. NeuroImage 63, 3 (2012), 1681–1694.
[19]
Rodrigo G. F. Soares, Huanhuan Chen, and Xin Yao. 2017. A cluster-based semisupervised ensemble for multiclass classification. IEEE Trans. Emerging Topics Comput. Intellig. 1, 6 (2017), 408–420.
[20]
Cynthia M. Stonnington, Carlton Chu, Stefan Klöppel, Clifford R. Jack Jr., John Ashburner, and Richard S. Frackowiak. 2010. Predicting clinical scores from magnetic resonance scans in Alzheimer’s disease. NeuroImage 51, 4 (2010), 1405–1413.
[21]
Maria Vounou, Eva Janousová, Robin Wolz, Jason L. Stein, Paul M. Thompson, Daniel Rueckert, and Giovanni Montana. 2012. Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease. NeuroImage 60, 1 (2012), 700–716.
[22]
Jing Wan, Zhilin Zhang, Jingwen Yan, Taiyong Li, Bhaskar D. Rao, Shiaofen Fang, Sungeun Kim, Shannon L. Risacher, Andrew J. Saykin, and Li Shen. 2012. Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer’s disease. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 940–947.
[23]
Hua Wang, Feiping Nie, Heng Huang, Shannon L. Risacher, Chris H. Q. Ding, Andrew J. Saykin, and Li Shen. 2011. Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. In IEEE International Conference on Computer Vision (ICCV’11), Dimitris N. Metaxas, Long Quan, Alberto Sanfeliu, and Luc Van Gool (Eds.). IEEE Computer Society, 557–562.
[24]
Liping Wang and Songcan Chen. 2013. matrix norm and its application in feature selection. CoRR abs/1303.3987 (2013).
[25]
Mingliang Wang, Daoqiang Zhang, Dinggang Shen, and Mingxia Liu. 2019. Multi-task exclusive relationship learning for Alzheimer’s disease progression prediction with longitudinal data. Med. Image Anal. 53 (2019), 111–122.
[26]
Yang Wang, Xiaodi Huang, and Lin Wu. 2013. Clustering via geometric median shift over Riemannian manifolds. Inf. Sci. 220 (2013), 292–305. Online Fuzzy Machine Learning and Data Mining.
[27]
Yang Wang, Xuemin Lin, Lin Wu, Wenjie Zhang, Qing Zhang, and Xiaodi Huang. 2015. Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Trans. Image Process. 24, 11 (2015), 3939–3949.
[28]
Yang Wang, Lin Wu, Xuemin Lin, and Junbin Gao. 2018. Multiview spectral clustering via structured low-rank matrix factorization. IEEE Trans. Neural Networks Learning Syst. 29, 10 (2018), 4833–4843.
[29]
Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin, Meng Fang, and Shirui Pan. 2016. Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI’16). AAAI Press, 2153–2159.
[30]
Lin Wu, Yang Wang, and Ling Shao. 2019. Cycle-consistent deep generative hashing for cross-modal retrieval. IEEE Trans. Image Process. 28, 4 (2019), 1602–1612.
[31]
Kun Zhan, Chaoxi Niu, Changlu Chen, Feiping Nie, Changqing Zhang, and Yi Yang. 2019. Graph structure fusion for multiview clustering. IEEE Trans. Knowl. Data Eng. 31, 10 (2019), 1984–1993.
[32]
Daoqiang Zhang and Dinggang Shen. 2012. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59, 2 (2012), 895–907.
[33]
Xiantong Zhen, Mengyang Yu, Xiaofei He, and Shuo Li. 2018. Multi-target regression via robust low-rank learning. IEEE Trans. Pattern Anal. Mach. Intell. 40, 2 (2018), 497–504.
[34]
Xiaofeng Zhu, Xuelong Li, and Shichao Zhang. 2016. Block-row sparse multiview multilabel learning for image classification. IEEE Trans. Cybernetics 46, 2 (2016), 450–461.
[35]
Xiaofeng Zhu, Heung-Il Suk, and Dinggang Shen. 2014. A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. NeuroImage 100 (2014), 91–105.
[36]
Xiaofeng Zhu, Heung-Il Suk, Li Wang, Seong-Whan Lee, and Dinggang Shen. 2017. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med. Image Anal. 38 (2017), 205–214.
[37]
Xiaofeng Zhu, Shichao Zhang, Rongyao Hu, Yonghua Zhu, and Jingkuan Song. 2018. Local and global structure preservation for robust unsupervised spectral feature selection. IEEE Trans. Knowl. Data Eng. 30, 3 (2018), 517–529.
[38]
Yue Zhu, James T. Kwok, and Zhi-Hua Zhou. 2018. Multi-label learning with global and local label correlation. IEEE Trans. Knowl. Data Eng. 30, 6 (2018), 1081–1094.

Cited By

View all
  • (2024)Reverse Graph Learning for Graph Neural NetworkIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.316103035:4(4530-4541)Online publication date: Apr-2024
  • (2024)Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer’s Disease DiagnosisIEEE Transactions on Image Processing10.1109/TIP.2024.338260033(2730-2745)Online publication date: 4-Apr-2024
  • (2024) Research on multi‐center assisted diagnosis of ASD based on multimodal feature fusion International Journal of Imaging Systems and Technology10.1002/ima.2311034:3Online publication date: 28-May-2024
  • Show More Cited By

Index Terms

  1. Adaptive Multi-Task Dual-Structured Learning with Its Application on Alzheimer’s Disease Study

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 21, Issue 2
      June 2021
      599 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3453144
      • Editor:
      • Ling Liu
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 24 May 2021
      Online AM: 16 May 2020
      Accepted: 01 May 2020
      Revised: 01 April 2020
      Received: 01 March 2020
      Published in TOIT Volume 21, Issue 2

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Multi-task learning
      2. Alzheimer’s predictive model

      Qualifiers

      • Research-article
      • Refereed

      Funding Sources

      • National key R&D Program of China
      • National Nature Science Foundation of China
      • Anhui Provincial Natural Science Foundation

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)22
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 16 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Reverse Graph Learning for Graph Neural NetworkIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.316103035:4(4530-4541)Online publication date: Apr-2024
      • (2024)Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer’s Disease DiagnosisIEEE Transactions on Image Processing10.1109/TIP.2024.338260033(2730-2745)Online publication date: 4-Apr-2024
      • (2024) Research on multi‐center assisted diagnosis of ASD based on multimodal feature fusion International Journal of Imaging Systems and Technology10.1002/ima.2311034:3Online publication date: 28-May-2024
      • (2022)Mixed-Net: A Mixed Architecture for Medical Image Segmentation2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995205(2095-2102)Online publication date: 6-Dec-2022
      • (2022)BLE-Net: boundary learning and enhancement network for polyp segmentationMultimedia Systems10.1007/s00530-022-00900-229:5(3041-3054)Online publication date: 25-Feb-2022
      • (2021)Forecasting Trend of Coronavirus Disease 2019 using Multi-Task Weighted TSK Fuzzy SystemACM Transactions on Internet Technology10.1145/347587022:3(1-24)Online publication date: 29-Nov-2021

      View Options

      Login options

      Full Access

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media