Summary of Balanced Graph Structure Information For Brain Disease Detection, by Falih Gozi Febrinanto et al.
Balanced Graph Structure Information for Brain Disease Detection
by Falih Gozi Febrinanto, Mujie Liu, Feng Xia
First submitted to arxiv on: 30 Dec 2023
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 This paper presents a novel approach to analyzing connections between brain regions of interest (ROI) for detecting neurological disorders such as autism or schizophrenia. The authors propose a graph neural network (GNN) model that leverages both filtered correlation matrices and optimal sample graphs to improve detection performances. This balanced approach, dubbed Bargrain, uses graph convolution networks (GCNs) to model two graph structures and address limitations of previous methods. The results show that Bargrain outperforms state-of-the-art methods in classification tasks on brain disease datasets, achieving higher average F1 scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bargrain is a new way to look at brain connections to help diagnose diseases like autism or schizophrenia. Instead of just using one type of connection, it uses two kinds: how much certain brain areas are connected and the best way to connect them. This helps the model learn more about the brain and make better predictions. |
Keywords
* Artificial intelligence * Classification * Gnn * Graph neural network
