Dynamic contrastive learning guided by class confidence and confusion degree for medical image segmentation
This work proposes an intra-Class-confidence and inter-Class-confusion guided Dynamic Contrastive (CCDC) learning framework for medical image segmentation. A core contribution is to dynamically select the most expressive pixels to build positive and negative pairs for contrastive learning at different training phases. For the positive pairs, dynamically adaptive sampling strategies are introduced for sampling different sets of pixels based on their hardness (namely the easiest, easy, and hard pixels). For the negative pairs, to efficiently learn from the classes with high confusion degree w.r.t a query class (i.e., a class containing the query pixels), a new hard class mining strategy is presented. Furthermore, pixel-level representations are extended to the neighbourhood region to leverage the spatial consistency of adjacent pixels. Extensive experiments on the three public datasets demonstrate that the proposed method significantly surpasses the state-of-the-art.
First ; Corresponding
National Key Research and Development Program of China[2021YFF1200800];National Natural Science Foundation of China;
|WOS Research Area|
Computer Science ; Engineering
Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
|WOS Accession No|
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Computer Science and Engineering|
1.Department of computer science and engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.WMG Visualization,University of Warwick,Coventry,CV4 7AL,United Kingdom
3.Department of Computer Science,University of Sheffield,Sheffield,S10 2TN,United Kingdom
4.Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,518055,China
5.Peng Cheng Lab,Shenzhen,518000,China
|First Author Affilication||Department of Computer Science and Engineering|
|Corresponding Author Affilication||Department of Computer Science and Engineering; Research Institute of Trustworthy Autonomous Systems|
|First Author's First Affilication||Department of Computer Science and Engineering|
Chen，Jingkun,Chen，Changrui,Huang，Wenjian,et al. Dynamic contrastive learning guided by class confidence and confusion degree for medical image segmentation[J]. Pattern Recognition,2024,145.
Chen，Jingkun,Chen，Changrui,Huang，Wenjian,Zhang，Jianguo,Debattista，Kurt,&Han，Jungong.(2024).Dynamic contrastive learning guided by class confidence and confusion degree for medical image segmentation.Pattern Recognition,145.
Chen，Jingkun,et al."Dynamic contrastive learning guided by class confidence and confusion degree for medical image segmentation".Pattern Recognition 145(2024).
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