Deep Ensemble Sparse Regression Network for Alzheimer’s Disease Diagnosis

  • Heung-Il SukEmail author
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)


For neuroimaging-based brain disease diagnosis, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of samples. In this paper, we propose a novel framework that utilizes sparse regression models as target-level representation learner and builds a deep convolutional neural network for clinical decision making. Specifically, we first train multiple sparse regression models, each of which has different values of a regularization control parameter, and use the outputs of the trained regression models as target-level representations. Note that sparse regression models trained with different values of a regularization control parameter potentially select different sets of features from the original ones, thereby they have different powers to predict the response values, i.e., a clinical label and clinical scores in our work. We then construct a deep convolutional neural network by taking the target-level representations as input. Our deep network learns to optimally fuse the predicted response variables, i.e., target-level representations, from the same sparse response model(s) and also those from the neighboring sparse response models. To our best knowledge, this is the first work that systematically integrates sparse regression models with deep neural network. In our experiments with ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest classification accuracies in three different tasks of Alzheimer’s disease and mild cognitive impairment identification.



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1C1A1A01052216) and also partially supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0101-15-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
  2. 2.Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillUSA

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