The paper presents a novel method called ContrastPool for classifying brain networks constructed from functional magnetic resonance imaging (fMRI) data. The key contributions are:
ContrastPool employs a contrastive dual-attention block that adaptively assigns weights to each region of interest (ROI) and each subject, performs aggregation within groups, and makes contrast across groups to obtain a contrast graph. This contrast graph is then used to guide the message passing in brain network representation learning.
The proposed differentiable graph pooling method, ContrastPool, generates brain network representations that are effective for disease classification tasks. It leverages the contrast graph to identify the most discriminative ROIs and subjects.
Experiments on 5 resting-state fMRI brain network datasets spanning 3 diseases (Parkinson's, Alzheimer's, and Autism) demonstrate the superiority of ContrastPool over state-of-the-art baselines.
A case study confirms that the patterns extracted by ContrastPool match the domain knowledge in neuroscience literature, providing direct and interesting insights into the underlying brain mechanisms of the studied diseases.
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by Jiax... lúc arxiv.org 04-15-2024
https://arxiv.org/pdf/2307.11133.pdfYêu cầu sâu hơn