Multi-model adaptive fusion-based graph network for Alzheimer's disease prediction.
Comput Biol Med
; 153: 106518, 2023 02.
Article
em En
| MEDLINE
| ID: mdl-36641934
ABSTRACT
Alzheimer's disease (AD) is a common cognitive disorder. Recently, many computer-aided diagnostic techniques have been used for AD prediction utilizing deep learning technology, among which graph neural networks have received increasing attention owing to their ability to model sample relationships on large population graphs. Most of the existing graph-based methods predict diseases according to a single model, which makes it difficult to select an appropriate node embedding algorithm for a certain classification task. Moreover, integrating data from different patterns into a unified model to improve the quality of disease diagnosis remains a challenge. Hence, in this study, we aimed to develop a multi-model fusion framework for AD prediction. A spectral graph attention model was used to aggregate intra- and inter-cluster node embeddings of normal and diseased populations, whereafter, a bilinear aggregation model was applied as an auxiliary model to enhance the abnormality degree in different categories of populations, and finally, an adaptive fusion module was designed to dynamically fuse the results of both models and enhance AD prediction. Compared to other comparison methods, the model proposed in this study provides the best results.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doença de Alzheimer
/
Disfunção Cognitiva
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article