{"type":"ScholarlyArticle","title":"Self-Promoted Clustering-based Contrastive Learning for Brain Networks Pretraining","authors":[{"name":"Junbo Ma","slug":"junbo-ma","given":"Junbo","family":"Ma","affiliation":"School of Communication Engineering , Hangzhou Dianzi University , Hangzhou 310018 , China; Lishui Institute of Hangzhou Dianzi University , Hangzhou Dianzi University , Hangzhou 310018 , China"},{"name":"Caixuan Luo","slug":"caixuan-luo","given":"Caixuan","family":"Luo","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541000 , China"},{"name":"Jia Hou","slug":"jia-hou","given":"Jia","family":"Hou","affiliation":"Lishui Institute of Hangzhou Dianzi University , Hangzhou Dianzi University , Hangzhou 310018 , China"},{"name":"Kai Zhao","slug":"kai-zhao","given":"Kai","family":"Zhao","affiliation":"Department of Neurosurgery , First Medical Center , Chinese PLA General Hospital , Beijing 100853 , China"}],"abstract":"Rapid advancements in neuroimaging techniques, such as magnetic resonance imaging (MRI), have facilitated the acquisition of the structural and functional characteristics of the brain. Brain network analysis is one of the essential tools for exploring brain mechanisms from MRI, providing valuable insights into the brain's organization, and stimulating the understanding of brain cognition and pathology of neurodegenerative diseases. Graph Neural Networks (GNNs) are commonly used for brain network analysis, but they are limited by the scarcity of medical data. Although Graph Contrastive Learning methods have been developed to address this, they often involve graph augmentations that distort the anatomical brain structures. To address these challenges, an augmentation-free contrastive learning method, named Self-Promoted Clustering-based Contrastive Learning(SPCCL), is proposed in this paper. Specifically, by introducing a clustering-based contrastive Learning loss and a self-promoted contrastive pairs creation scheme, the proposed SPCCL can be pre-trained from additional healthy subjects' data that are relatively easier to acquire than disorder ones. The proposed SPCCL leverages these additional data with respect to the integrity of the original brain structure, making it a promising approach for effective brain network analysis. Comprehensive experiments are conducted on an open-access schizophrenic dataset, demonstrating the effectiveness of the proposed method.","doi":"10.24963/ijcai.2024/129","url":"https://ijcai.org/proceedings/2024/129","pdf":"https://ijcai.org/proceedings/2024/0129.pdf","markdown":"https://ijcai.org/proceedings/2024/129.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/129","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":129,"pages":{"first":1164,"last":1172},"keywords":["Machine Learning: ML: Deep learning architectures","Machine Learning: ML: Multi-modal learning","Humans and AI: HAI: Brain sciences","Computer Vision: CV: Biomedical image analysis"],"videos":[],"references":[{"position":1,"raw":"[Ahmedt-Aristizabal et al., 2021] David Ahmedt- Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, and Lars Petersson. 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