Weipeng Jing , Junze Wang , Donglin Di , Dandan Li , Yang Song , Lei Fan
{"title":"Multi-modal hypergraph contrastive learning for medical image segmentation","authors":"Weipeng Jing , Junze Wang , Donglin Di , Dandan Li , Yang Song , Lei Fan","doi":"10.1016/j.patcog.2025.111544","DOIUrl":null,"url":null,"abstract":"<div><div>Self-supervised learning (SSL) has become a dominant approach in multi-modal medical image segmentation. However, existing methods, such as Seq SSL and Joint SSL, suffer from catastrophic forgetting and conflicts in representation learning across different modalities. To address these challenges, we propose a two-stage SSL framework, HyCon, for multi-modal medical image segmentation. It combines the advantages of Seq and Joint SSL using knowledge distillation to align similar topological samples across modalities. In the first stage, cross-modal features are learned through adversarial learning. Inspired by the Graph Foundation Models and further adapted to our task, the Hypergraph Contrastive Learning Network (HCLN) with a teacher-student architecture is subsequently introduced to capture high-order relationships across modalities by integrating hypergraphs with contrastive learning. The Topology Hybrid Distillation (THD) module distills topological information, contextual features, and relational knowledge into the student model. We evaluated HyCon on two organs, lung and brain. Our framework outperformed state-of-the-art SSL methods, achieving significant improvements in segmentation with limited labeled data. Both quantitative and qualitative experiments validate the effectiveness of the design of our framework. Code is available at: <span><span>https://github.com/reeive/HyCon</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"165 ","pages":"Article 111544"},"PeriodicalIF":7.5000,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320325002043","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Self-supervised learning (SSL) has become a dominant approach in multi-modal medical image segmentation. However, existing methods, such as Seq SSL and Joint SSL, suffer from catastrophic forgetting and conflicts in representation learning across different modalities. To address these challenges, we propose a two-stage SSL framework, HyCon, for multi-modal medical image segmentation. It combines the advantages of Seq and Joint SSL using knowledge distillation to align similar topological samples across modalities. In the first stage, cross-modal features are learned through adversarial learning. Inspired by the Graph Foundation Models and further adapted to our task, the Hypergraph Contrastive Learning Network (HCLN) with a teacher-student architecture is subsequently introduced to capture high-order relationships across modalities by integrating hypergraphs with contrastive learning. The Topology Hybrid Distillation (THD) module distills topological information, contextual features, and relational knowledge into the student model. We evaluated HyCon on two organs, lung and brain. Our framework outperformed state-of-the-art SSL methods, achieving significant improvements in segmentation with limited labeled data. Both quantitative and qualitative experiments validate the effectiveness of the design of our framework. Code is available at: https://github.com/reeive/HyCon.
期刊介绍:
The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.