Summary of Contrasformer: a Brain Network Contrastive Transformer For Neurodegenerative Condition Identification, by Jiaxing Xu et al.
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
by Jiaxing Xu, Kai He, Mengcheng Lan, Qingtian Bian, Wei Li, Tieying Li, Yiping Ke, Miao Qiao
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract proposes a novel Transformer model, Contrasformer, designed to analyze brain networks from functional magnetic resonance imaging (fMRI) data. The main challenge is addressing noise in datasets caused by distribution shifts across sub-populations and neglecting node identities. To tackle this, the proposed model generates a prior-knowledge-enhanced contrast graph using a two-stream attention mechanism and incorporates identity embeddings through cross attention. Three auxiliary losses ensure group consistency. Evaluated on four functional brain network datasets over four different diseases, Contrasformer outperforms state-of-the-art methods by up to 10.8% in accuracy. Case studies illustrate its interpretability, providing insights into neurological disorders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Contrasformer is a new way to analyze brain networks from fMRI data. Right now, it’s hard to understand brain networks because of noise in the data that makes it hard to find patterns related to specific diseases. Contrasformer uses a special kind of graph network called a Transformer to help with this problem. It does this by creating a “prior-knowledge-enhanced contrast graph” that helps remove noise and keep track of important node identities. The model is tested on several different brain networks and shows big improvements over current methods, giving us new insights into neurological disorders. |
Keywords
» Artificial intelligence » Attention » Cross attention » Transformer
