ArticleProceedings. IEEE International Symposium on Biomedical Imaging2025
USING STRUCTURAL SIMILARITY AND KOLMOGOROV-ARNOLD NETWORKS FOR ANATOMICAL EMBEDDING OF CORTICAL FOLDING PATTERNS.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- DEEP-GYRALNET: ENABLING MACHINE LEARNING IN GYRAL FOLDING PATTERN EXTRACTION ON CORTICAL SURFACE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- A Unified Continuous Staging Framework for Alzheimer's Disease and Lewy Body Dementia via Hierarchical Anatomical Features.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Core-Periphery Principle Guided State Space Model for Functional Connectome Classification.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Fine-scale individualized gyral folding-based cortical similarity networks reveal distinct organizational patterns in Alzheimer's disease and Lewy body dementia.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
10 authors.
Funding
Abstract
The 3-hinge gyrus (3HG) is a newly defined folding pattern, which is the conjunction of gyri coming from three directions in cortical folding. Many studies demonstrated that 3HGs can be reliable nodes when constructing brain networks or connectome since they simultaneously possess commonality and individuality across different individual brains and populations. However, 3HGs are identified and validated within individual spaces, making it difficult to directly serve as the brain network nodes due to the absence of cross-subject correspondence. The 3HG correspondences represent the intrinsic regulation of brain organizational architecture, traditional image-based registration methods tend to fail because individual anatomical properties need to be fully respected. To address this challenge, we propose a novel self-supervised framework for anatomical feature embedding of the 3HGs to build the correspondences among different brains. The core component of this framework is to construct a structural similarity-enhanced multi-hop feature encoding strategy based on the recently developed Kolmogorov-Arnold network (KAN) for anatomical feature embedding. Extensive experiments suggest that our approach can effectively establish robust cross-subject correspondences when no one-to-one mapping exists. The code is available at github.com/m1nhengChen/SSE-CortexEmbed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.