Summary of Using Structural Similarity and Kolmogorov-arnold Networks For Anatomical Embedding Of Cortical Folding Patterns, by Minheng Chen et al.
Using Structural Similarity and Kolmogorov-Arnold Networks for Anatomical Embedding of Cortical Folding Patterns
by Minheng Chen, Chao Cao, Tong Chen, Yan Zhuang, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Tianming Liu, Dajiang Zhu
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed self-supervised framework tackles the challenge of establishing correspondences among different brains’ 3-hinge gyrus (3HGs) by constructing a structural similarity-enhanced multi-hop feature encoding strategy based on the Kolmogorov-Arnold network (KAN). This enables robust cross-subject correspondences even when no one-to-one mapping exists. The framework builds upon recent developments in anatomical feature embedding and demonstrates effective establishment of 3HG correspondences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about finding a way to match similar brain patterns, called 3HGs, across different people’s brains. Right now, it’s hard to do this because each person’s brain is slightly different. To solve this problem, the researchers created a new method that uses special math and computer algorithms to compare brain features. They tested their approach and found that it can successfully match similar patterns across different brains, even when there isn’t a direct one-to-one match. |
Keywords
» Artificial intelligence » Embedding » Self supervised
