A Generative Imputation Method for Multimodal Alzheimer's Disease Diagnosis
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918124275630080 |
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| author | Hassanzadeh, Reihaneh Abrol, Anees Hassanzadeh, Hamid Reza Calhoun, Vince D. |
| author_facet | Hassanzadeh, Reihaneh Abrol, Anees Hassanzadeh, Hamid Reza Calhoun, Vince D. |
| contents | Multimodal data analysis can lead to more accurate diagnoses of brain disorders due to the complementary information that each modality adds. However, a major challenge of using multimodal datasets in the neuroimaging field is incomplete data, where some of the modalities are missing for certain subjects. Hence, effective strategies are needed for completing the data. Traditional methods, such as subsampling or zero-filling, may reduce the accuracy of predictions or introduce unintended biases. In contrast, advanced methods such as generative models have emerged as promising solutions without these limitations. In this study, we proposed a generative adversarial network method designed to reconstruct missing modalities from existing ones while preserving the disease patterns. We used T1-weighted structural magnetic resonance imaging and functional network connectivity as two modalities. Our findings showed a 9% improvement in the classification accuracy for Alzheimer's disease versus cognitive normal groups when using our generative imputation method compared to the traditional approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09271 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Generative Imputation Method for Multimodal Alzheimer's Disease Diagnosis Hassanzadeh, Reihaneh Abrol, Anees Hassanzadeh, Hamid Reza Calhoun, Vince D. Image and Video Processing Machine Learning Multimodal data analysis can lead to more accurate diagnoses of brain disorders due to the complementary information that each modality adds. However, a major challenge of using multimodal datasets in the neuroimaging field is incomplete data, where some of the modalities are missing for certain subjects. Hence, effective strategies are needed for completing the data. Traditional methods, such as subsampling or zero-filling, may reduce the accuracy of predictions or introduce unintended biases. In contrast, advanced methods such as generative models have emerged as promising solutions without these limitations. In this study, we proposed a generative adversarial network method designed to reconstruct missing modalities from existing ones while preserving the disease patterns. We used T1-weighted structural magnetic resonance imaging and functional network connectivity as two modalities. Our findings showed a 9% improvement in the classification accuracy for Alzheimer's disease versus cognitive normal groups when using our generative imputation method compared to the traditional approaches. |
| title | A Generative Imputation Method for Multimodal Alzheimer's Disease Diagnosis |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2508.09271 |