Semi-supervised learning(SSL)aims to improve performance by exploiting unlabeled data when labels are scarce.Conventional SSL studies typically assume close environments where important factors(e.g.,label,feature,dist...Semi-supervised learning(SSL)aims to improve performance by exploiting unlabeled data when labels are scarce.Conventional SSL studies typically assume close environments where important factors(e.g.,label,feature,distribution)between labeled and unlabeled data are consistent.However,more practical tasks involve open environments where important factors between labeled and unlabeled data are inconsistent.It has been reported that exploiting inconsistent unlabeled data causes severe performance degradation,even worse than the simple supervised learning baseline.Manually verifying the quality of unlabeled data is not desirable,therefore,it is important to study robust SSL with inconsistent unlabeled data in open environments.This paper briefly introduces some advances in this line of research,focusing on techniques concerning label,feature,and data distribution inconsistency in SSL,and presents the evaluation benchmarks.Open research problems are also discussed for reference purposes.展开更多
Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adep...Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adept at leveraging a modest amount of labeled data.Nonetheless,most prevailing SSL segmentation methods for medical images either rely on the single consistency training method or directly fine-tune SSL methods designed for natural images.In this paper,we propose an innovative semi-supervised method called multi-consistency training(MCT)for medical image segmentation.Our approach transcends the constraints of prior methodologies by considering consistency from a dual perspective:output consistency across different up-sampling methods and output consistency of the same data within the same network under various perturbations to the intermediate features.We design distinct semi-supervised loss regression methods for these two types of consistencies.To enhance the application of our MCT model,we also develop a dedicated decoder as the core of our neural network.Thorough experiments were conducted on the polyp dataset and the dental dataset,rigorously compared against other SSL methods.Experimental results demonstrate the superiority of our approach,achieving higher segmentation accuracy.Moreover,comprehensive ablation studies and insightful discussion substantiate the efficacy of our approach in navigating the intricacies of medical image segmentation.展开更多
基金supported by the Key Program of Jiangsu Science Foundation(BK20243012)the National Natural Science Foundation of China(NSFC)(Grant Nos.62306133,62176118).
文摘Semi-supervised learning(SSL)aims to improve performance by exploiting unlabeled data when labels are scarce.Conventional SSL studies typically assume close environments where important factors(e.g.,label,feature,distribution)between labeled and unlabeled data are consistent.However,more practical tasks involve open environments where important factors between labeled and unlabeled data are inconsistent.It has been reported that exploiting inconsistent unlabeled data causes severe performance degradation,even worse than the simple supervised learning baseline.Manually verifying the quality of unlabeled data is not desirable,therefore,it is important to study robust SSL with inconsistent unlabeled data in open environments.This paper briefly introduces some advances in this line of research,focusing on techniques concerning label,feature,and data distribution inconsistency in SSL,and presents the evaluation benchmarks.Open research problems are also discussed for reference purposes.
基金the Innovation Program of Shanghai Industrial Synergy(No.XTCX-KJ-2023-2-12)。
文摘Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adept at leveraging a modest amount of labeled data.Nonetheless,most prevailing SSL segmentation methods for medical images either rely on the single consistency training method or directly fine-tune SSL methods designed for natural images.In this paper,we propose an innovative semi-supervised method called multi-consistency training(MCT)for medical image segmentation.Our approach transcends the constraints of prior methodologies by considering consistency from a dual perspective:output consistency across different up-sampling methods and output consistency of the same data within the same network under various perturbations to the intermediate features.We design distinct semi-supervised loss regression methods for these two types of consistencies.To enhance the application of our MCT model,we also develop a dedicated decoder as the core of our neural network.Thorough experiments were conducted on the polyp dataset and the dental dataset,rigorously compared against other SSL methods.Experimental results demonstrate the superiority of our approach,achieving higher segmentation accuracy.Moreover,comprehensive ablation studies and insightful discussion substantiate the efficacy of our approach in navigating the intricacies of medical image segmentation.
