Incomplete Multi-modal Disentanglement Learning with Application to Alzheimer's Disease Diagnosis
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces Incomplete Multi-modal Disentanglement Learning (IMDL) for Alzheimer's disease (AD) diagnosis using incomplete neuroimaging data. IMDL effectively diagnoses AD without synthesizing missing scans, improving upon conventional methods.
Area Of Science
- Neuroimaging
- Artificial Intelligence
- Medical Diagnostics
Background
- Multi-modal neuroimaging (MRI, PET) aids Alzheimer's disease (AD) diagnosis.
- Incomplete data is a significant challenge in current computer-aided diagnosis.
- Existing methods for handling missing data reduce sample size or introduce noise.
Purpose Of The Study
- To develop a novel method for AD diagnosis using incomplete multi-modal neuroimaging data.
- To address the limitations of conventional strategies for handling missing data in neuroimaging analysis.
- To improve the accuracy and robustness of computer-aided diagnosis for AD.
Main Methods
- Proposing Incomplete Multi-modal Disentanglement Learning (IMDL) for AD diagnosis.
- Utilizing modality-wise variational autoencoders and a Transformer for feature fusion.
- Implementing cross-modality contrastive learning and adversarial learning to harmonize representations.
- Developing a local attention rectification module for enhanced localization of atrophic areas.
Main Results
- IMDL demonstrated superior performance in AD diagnosis on ADNI and AIBL datasets.
- The method effectively handles incomplete multi-modal neuroimaging data without scan synthesis.
- Validation on the HABS-HD dataset showed effectiveness for general dementia diagnosis with different imaging modalities.
Conclusions
- IMDL offers a robust and effective solution for AD diagnosis with incomplete multi-modal neuroimaging data.
- The proposed method overcomes limitations of traditional approaches by avoiding sample reduction and noise introduction.
- IMDL shows promise for improving diagnostic accuracy and applicability across different neuroimaging datasets and dementia types.
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