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A coupled-GAN architecture to fuse MRI and PET image features for multi-stage classification of Alzheimer's disease.

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Title: A coupled-GAN architecture to fuse MRI and PET image features for multi-stage classification of Alzheimer's disease.
Authors: Choudhury, Chandrajit1 (AUTHOR) chandrajit@ece.nits.ac.in, Goel, Tripti1 (AUTHOR) triptigoel@ece.nits.ac.in, Tanveer, M.2 (AUTHOR) mtanveer@iiti.ac.in
Source: Information Fusion. Sep2024, Vol. 109, pN.PAG-N.PAG. 1p.
Subjects: Alzheimer's disease, Positron emission tomography, Magnetic resonance imaging, Multimodal user interfaces, Feature extraction, Mild cognitive impairment, Neurofibrillary tangles, Data extraction
Abstract: Alzheimer's disease (AD) is a degenerative neurological ailment that begins with memory loss and ultimately leads to a total loss of mental capacity. Researchers are interested in using magnetic resonance imaging (MRI) and positron emission tomography (PET) to find people with mild cognitive impairment (MCI), which is a stage before Alzheimer's disease (AD). Significant hippocampal loss and temporal lobe atrophy characterize the transition from MCI to AD, which can be visualized using T1-W structural MRI. PET visualizes brain glucose metabolism, which indicates neuronal activity, making it a viable neuroimaging method for AD diagnosis. The extraction and fusion of structural and metabolite information about brain alterations contained in multimodal data is crucial for achieving an appropriate classification result. Therefore, in this work a new end-to-end coupled-GAN (CGAN) architecture is introduced. The proposed CGANC network consists of two sub-models: a CGAN for extraction of fused features from multimodal data, and a CNN classifier to classify these features. The proposed CGAN model is trained to encode MRI and PET images into a shared latent space. The fused features are extracted from this shared latent space and then are classified according to particular stage of AD. In order to test the effectiveness of the suggested approach, experiments are done on the publicly available ADNI dataset and compared with state-of-the-art methods. The proposed method's source code will be made freely available at https://github.com/ChandrajitChoudhury/CGAN-AD. • MRI and PET images are fused to utilize structural and metabolic features for AD diagnosis. • An adversarial learning-based method is proposed to extract fused features. • A coupled adversarial subnetwork and a classification subnetwork have been designed. • Experiments are done on the publicly available ADNI dataset. [ABSTRACT FROM AUTHOR]
Copyright of Information Fusion is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Alzheimer's disease (AD) is a degenerative neurological ailment that begins with memory loss and ultimately leads to a total loss of mental capacity. Researchers are interested in using magnetic resonance imaging (MRI) and positron emission tomography (PET) to find people with mild cognitive impairment (MCI), which is a stage before Alzheimer's disease (AD). Significant hippocampal loss and temporal lobe atrophy characterize the transition from MCI to AD, which can be visualized using T1-W structural MRI. PET visualizes brain glucose metabolism, which indicates neuronal activity, making it a viable neuroimaging method for AD diagnosis. The extraction and fusion of structural and metabolite information about brain alterations contained in multimodal data is crucial for achieving an appropriate classification result. Therefore, in this work a new end-to-end coupled-GAN (CGAN) architecture is introduced. The proposed CGANC network consists of two sub-models: a CGAN for extraction of fused features from multimodal data, and a CNN classifier to classify these features. The proposed CGAN model is trained to encode MRI and PET images into a shared latent space. The fused features are extracted from this shared latent space and then are classified according to particular stage of AD. In order to test the effectiveness of the suggested approach, experiments are done on the publicly available ADNI dataset and compared with state-of-the-art methods. The proposed method's source code will be made freely available at https://github.com/ChandrajitChoudhury/CGAN-AD. • MRI and PET images are fused to utilize structural and metabolic features for AD diagnosis. • An adversarial learning-based method is proposed to extract fused features. • A coupled adversarial subnetwork and a classification subnetwork have been designed. • Experiments are done on the publicly available ADNI dataset. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Information Fusion is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.inffus.2024.102415
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      – Code: eng
        Text: English
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        StartPage: N.PAG
    Subjects:
      – SubjectFull: Alzheimer's disease
        Type: general
      – SubjectFull: Positron emission tomography
        Type: general
      – SubjectFull: Magnetic resonance imaging
        Type: general
      – SubjectFull: Multimodal user interfaces
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Mild cognitive impairment
        Type: general
      – SubjectFull: Neurofibrillary tangles
        Type: general
      – SubjectFull: Data extraction
        Type: general
    Titles:
      – TitleFull: A coupled-GAN architecture to fuse MRI and PET image features for multi-stage classification of Alzheimer's disease.
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            NameFull: Choudhury, Chandrajit
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            NameFull: Goel, Tripti
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            NameFull: Tanveer, M.
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            – D: 01
              M: 09
              Text: Sep2024
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              Y: 2024
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              Value: 109
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