Transformer-Based Hierarchical Clustering for Brain Network Analysis

Autor:	Dai, Wei, Cui, Hejie, Kan, Xuan, Guo, Ying, van Rooij, Sanne, Yang, Carl
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Computer Science - Neural and Evolutionary Computing Quantitative Biology - Neurons and Cognition 68T07 68T45 68T20 I.2.6 I.2.10 J.3
Druh dokumentu:	Working Paper
Popis:	Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions. Within the complex brain system, differences in neuronal connection strengths parcellate the brain into various functional modules (network communities), which are critical for brain analysis. However, identifying such communities within the brain has been a nontrivial issue due to the complexity of neuronal interactions. In this work, we propose a novel interpretable transformer-based model for joint hierarchical cluster identification and brain network classification. Extensive experimental results on real-world brain network datasets show that with the help of hierarchical clustering, the model achieves increased accuracy and reduced runtime complexity while providing plausible insight into the functional organization of brain regions. The implementation is available at https://github.com/DDVD233/THC. Comment: Accepted to IEEE-ISBI 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.04142 Zobrazit plný text záznamu View this record from Arxiv