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Ensemble deep learning for Alzheimer’s disease characterization and estimation

Nature Mental Health (2024)Cite this article

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Abstract

Alzheimer’s disease, which is characterized by a continual deterioration of cognitive abilities in older people, is the most common form of dementia. Neuroimaging data, for example, from magnetic resonance imaging and positron emission tomography, enable identification of the structural and functional changes caused by Alzheimer’s disease in the brain. Diagnosing Alzheimer’s disease is critical in medical settings, as it supports early intervention and treatment planning and contributes to expanding our knowledge of the dynamics of Alzheimer’s disease in the brain. Lately, ensemble deep learning has become popular for enhancing the performance and reliability of Alzheimer’s disease diagnosis. These models combine several deep neural networks to increase a prediction’s robustness. Here we revisit key developments of ensemble deep learning, connecting its design—the type of ensemble, its heterogeneity and data modalities—with its application to AD diagnosis using neuroimaging and genetic data. Trends and challenges are discussed thoroughly to assess where our knowledge in this area stands.

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Fig. 1: Ensemble deep learning strategies.
Fig. 2: Feature learning approaches from 3D MRI scans.
Fig. 3: Challenges and future directions of ensemble deep learning for AD diagnosis.

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Acknowledgements

This study was funded by the National Supercomputing Mission under DST and Miety, the Government of India under grant no. DST/NSM/R&D_HPC_Appl/ 2021/03.29 and the Science and Engineering Research Board (SERB), Government of India, under the Ramanujan Fellowship Scheme, grant no. SB/S2/RJN-001/2016. J.D.S. acknowledges funding support from the Spanish Centro para el Desarrollo Tecnológico Industrial (CDTI) through the AI4ES project, as well as from the Department of Education of the Basque Government (consolidated research group MATHMODE, IT1456-22). C.T.L. acknowledges funding support from the Australian Research Council (ARC, DP220100803), Australian National Health and Medical Research Council (NHMRC) Ideas Grant APP2021183, and the UTS Human-Centric AI Centre, sponsored by GrapheneX (2023–2031). We also extend our sincere appreciation to M. A. Ganaie, D. Pranjal and H. Kolukuluru for their help in the initial stages of this work.

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  1. These authors contributed equally: T. Goel, R. Sharma, A. K. Malik.

Authors and Affiliations

  1. OPTIMAL Research Lab, Department of Mathematics, Indian Institute of Technology Indore, Simrol, India

    M. Tanveer, R. Sharma & A. K. Malik

  2. Biomedical Imaging Lab, National Institute of Technology Silchar, Assam, India

    T. Goel

  3. Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada

    I. Beheshti

  4. TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Spain

    J. Del Ser

  5. University of the Basque Country (UPV/EHU), Bilbao, Spain

    J. Del Ser

  6. KINDI Center for Computing Research, College of Engineering, Qatar University, Doha, Qatar

    P. N. Suganthan

  7. Human-centric AI (HAI) Centre, Australia Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia

    C. T. Lin

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Tanveer, M., Goel, T., Sharma, R. et al. Ensemble deep learning for Alzheimer’s disease characterization and estimation. Nat. Mental Health (2024). https://doi.org/10.1038/s44220-024-00237-x

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