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A Weakly Supervised Deep Learning Model for Alzheimer’s Disease Prognosis Using MRI and Incomplete Labels

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Neural Information Processing (ICONIP 2023)

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Abstract

Predicting cognitive scores using magnetic resonance imaging (MRI) can aid in the early recognition of Alzheimer’s disease (AD) and provide insights into future disease progression. Existing methods typically ignore the temporal consistency of cognitive scores and discard the subjects with incomplete cognitive scores. In this paper, we propose a Weakly supervised Alzheimer’s Disease Prognosis (WADP) model that incorporates an image embedding network and a label embedding network to predict cognitive scores using baseline MRI and incomplete cognitive scores. The image embedding network is an attention consistency regularized network to project MRI into the image embedding space and output the cognitive scores at multiple time-points. The attention consistency regularization captures the correlations among time-points by encouraging the attention maps at different time-points to be similar. The label embedding network employs a denoising autoencoder to embed cognitive scores into the label embedding space and impute missing cognitive scores. This enables the utilization of subjects with incomplete cognitive scores in the training process. Moreover, a relation alignment module is incorporated to make the relationships between samples in the image embedding space consistent with those in the label embedding space. The experimental results on two ADNI datasets show that WADP outperforms the state-of-the-art methods.

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Acknowledgments

This research was supported in part by the National Key R &D Program of China under grant 2019YFC1710300, Yibin Science and Technology Plan Project under grant 2022ZYD10, Key Laboratory of State Administration of Traditional Chinese Medicine for Scientific Research & Industrial Development of Traditional Chinese Medicine Regimen and Health under grant GZ2022009, Key Laboratory of Sichuan Province for Traditional Chinese Medicine Regimen and Health under grant GZ2022009 and the Sichuan Science and Technology Program under grants 2020YFS0283, 2021YJ0184, 2021YFS0152, and 2019YFS0019.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this paper.

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Authors and Affiliations

  1. Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Zhi Chen, Yongguo Liu, Yun Zhang, Jiajing Zhu & Qiaoqin Li

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  1. Zhi Chen
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  2. Yongguo Liu
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  3. Yun Zhang
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  4. Jiajing Zhu
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  5. Qiaoqin Li
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Corresponding author

Correspondence to Yongguo Liu .

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Editors and Affiliations

  1. Central South University, Changsha, China

    Biao Luo

  2. Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Chen, Z., Liu, Y., Zhang, Y., Zhu, J., Li, Q. (2024). A Weakly Supervised Deep Learning Model for Alzheimer’s Disease Prognosis Using MRI and Incomplete Labels. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_13

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  • DOI: https://doi.org/10.1007/978-981-99-8067-3_13

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