Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases
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Summary
The experimental results show that the proposed deep multimodality-disentangled association analysis network (DMAAN) can identify the disease-related biomarkers, which suggests the proposed DMAAN may provide new insights into the pathological mechanism and early diagnosis of NDs.
- Type
- article
- Published
- 2023-05-01
- Cited by
- 19
- References
- 89
- OpenAlex
- https://openalex.org/W4377101153
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:258814590
Keywords
Modality (human–computer interaction), Imaging genetics, Multimodality, Computer science, Artificial intelligence
References
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Cited by
- TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance
- Towards interpretable imaging genomics analysis: Methodological developments and applications
- Applications of interpretable deep learning in neuroimaging: A comprehensive review
- Dual Attention Graph Convolutional Network Fusing Imaging and Genetic Data for Early Alzheimer’s Disease Diagnosis
- Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.
- Inspired by pathogenic mechanisms: A novel gradual multi-modal fusion framework for mild cognitive impairment diagnosis
- Disentanglement and codebook learning-induced feature match network to diagnose neurodegenerative diseases on incomplete multimodal data
- A disentanglement mamba network with a temporally slack reconstruction mechanism for multimodal continuous emotion recognition
- Incomplete Multi-Modal Disentanglement Learning With Application to Alzheimer’s Disease Diagnosis
- Adaptive latent disease state learning for multimodal Alzheimer's disease biomarker detection with missing modalities
- Multimodal adaptive fusion deep analysis model for Alzheimer's disease exploration and diagnosis
- Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review
- Intermodal correlation modeling for incomplete multi-modal learning in land use and land cover classification
- Hippocampal surface morphological variation-based genome-wide association analysis network for biomarker detection of Alzheimer's disease
- Channel-wise joint disentanglement representation learning for B-mode and super-resolution ultrasound based CAD of breast cancer
- Multi-Modal Fusion with Supervised Contrastive Learning Model for Early Alzheimer's Disease Diagnosis and Multi-Modal Biomarker Identification.
- Individualized Brain Asymmetry Modeling with Multimodal Pathological Consistency for Alzheimer’s Disease Diagnosis
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