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Learning using privileged information improves neuroimaging-based CAD of Alzheimer’s disease: a comparative study

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Abstract

The neuroimaging-based computer-aided diagnosis (CAD) for Alzheimer’s disease (AD) has shown its effectiveness in recent years. In general, the multimodal neuroimaging-based CAD always outperforms the approaches based on a single modality. However, single-modal neuroimaging is more favored in clinical practice for diagnosis due to the limitations of imaging devices, especially in rural hospitals. Learning using privileged information (LUPI) is a new learning paradigm that adopts additional privileged information (PI) modality to help to train a more effective learning model during the training stage, but PI itself is not available in the testing stage. Since PI is generally related to the training samples, it is then transferred to the learned model. In this work, a LUPI-based CAD framework for AD is proposed. It can flexibly perform a classifier- or feature-level LUPI, in which the information is transferred from the additional PI modality to the diagnosis modality. A thorough comparison has been made among three classifier-level algorithms and five feature-level LUPI algorithms. The experimental results on the ADNI dataset show that all classifier-level and deep learning based feature-level LUPI algorithms can improve the performance of a single-modal neuroimaging-based CAD for AD by transferring PI.

Graphical abstract for the framework of the LUPI-based CAD for AD

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Funding

This work is supported by the National Natural Science Foundation of China (61471231, 81627804, 61471245) and the Shanghai Science and Technology Foundation (17411953400, 18010500600).

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Authors and Affiliations

  1. Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Yan Li

  2. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, No. 99 Shangda Road, Shanghai, 200444, People’s Republic of China

    Fanqing Meng & Jun Shi

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  1. Yan Li
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  2. Fanqing Meng
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  3. Jun Shi
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Alzheimer’s Disease Neuroimaging Initiative

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Correspondence to Jun Shi.

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Li, Y., Meng, F., Shi, J. et al. Learning using privileged information improves neuroimaging-based CAD of Alzheimer’s disease: a comparative study. Med Biol Eng Comput 57, 1605–1616 (2019). https://doi.org/10.1007/s11517-019-01974-3

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  • DOI: https://doi.org/10.1007/s11517-019-01974-3

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