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Enhanced semi-supervised learning for top gas flow state classification to optimize emission and production in blast ironmaking furnaces 认领 引用
1
作者 Song Liu Qiqi Li +3 位作者 Qing Ye Zhiwei Zhao Dianyu E Shibo Kuang 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第1期204-216,共13页
Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate ... Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics. 展开更多
关键词 blast furnace gas flow state semi-supervised learning mean teacher feature loss
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Semi-Supervised Segmentation Framework for Quantitative Analysis of Material Microstructure Images 认领 引用
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作者 Yingli Liu Weiyong Tang +2 位作者 Xiao Yang Jiancheng Yin Haihe Zhou 《Computers, Materials & Continua》 SCIE EI 2026年第4期596-611,共16页
Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subje... Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subjective,while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data.Furthermore,existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data.To address these issues,this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation.First,we introduce a Rotational Uncertainty Correction Strategy(RUCS).This strategy employs multi-angle rotational perturbations andMonte Carlo sampling to assess prediction consistency,generating a pixel-wise confidence weight map.By integrating this map into the loss function,the model dynamically focuses on high-confidence regions,thereby improving generalization ability while reducing manual annotation pressure.Second,we design a Boundary EnhancementModule(BEM)to strengthen boundary feature extraction through erosion difference and multi-scale dilated convolutions.This module guides the model to focus on the boundary regions of adjacent particles,effectively resolving particle adhesion and improving segmentation accuracy.Systematic experiments were conducted on the Aluminum-Silicon Alloy Microstructure Dataset(ASAD).Results indicate that the proposed method performs exceptionally well with scarce labeled data.Specifically,using only 5%labeled data,our method improves the Jaccard index and Adjusted Rand Index(ARI)by 2.84 and 1.57 percentage points,respectively,and reduces the Variation of Information(VI)by 8.65 compared to stateof-the-art semi-supervised models,approaching the performance levels of 10%labeled data.These results demonstrate that the proposed method significantly enhances the accuracy and robustness of quantitative microstructure analysis while reducing annotation costs. 展开更多
关键词 Microstructure alloy semi-supervised segmentation boundary enhancement variation of information
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A Novel Semi-Supervised Multi-View Picture Fuzzy Clustering Approach for Enhanced Satellite Image Segmentation 认领 引用
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作者 Pham Huy Thong Hoang Thi Canh +2 位作者 Nguyen Tuan Huy Nguyen Long Giang Luong Thi Hong Lan 《Computers, Materials & Continua》 SCIE EI 2026年第3期1092-1117,共26页
Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel... Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping. 展开更多
关键词 Multi-view clustering satellite image segmentation semi-supervised learning picture fuzzy sets remote sensing
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Federated Semi-Supervised Learning Based on Feature Space Fusion 认领 引用
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作者 Zhe Ding Hao Yi +6 位作者 Wenrui Xie Ming Zhang Yuxuan Xiao Qixu Wang Qing Chen Zhiguang Qin Dajiang Chen 《Computers, Materials & Continua》 SCIE EI 2026年第5期2062-2076,共15页
Federated semi-supervised learning(FSSL)has garnered substantial attention for enabling collaborative global model training across multiple clients to address the scarcity of labeled data and to preserve data privacy.... Federated semi-supervised learning(FSSL)has garnered substantial attention for enabling collaborative global model training across multiple clients to address the scarcity of labeled data and to preserve data privacy.However,FSSL is plagued by formidable challenges stemming fromcross-client data heterogeneity,as existing methods fail to achieve effective fusion of feature subspaces across distinct clients.To address this issue,we propose a novel FSSL framework,named FedSPQR,which is explicitly tailored for the label-at-server scenario.On the server side,FedSPQR adopts subspace clustering and fusion method based on the Grassmann manifold to construct a unified global feature space,which is further leveraged to refine the global model.On the client side,the pre-established global feature space acts as a benchmark for aligning the local feature subspaces.Based on the aligned local feature subspaces,integrating self-supervised learning with knowledge distillation facilitates effective local learning to alleviate local bias caused by data heterogeneity.Extensive experiments on two standard public benchmarks confirm that FedSPQR outperforms state-of-the-art(SOTA)baselines by a significant margin. 展开更多
关键词 Federated semi-supervised learning feature space fusion knowledge distillation
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Data-driven estimation of surrounding rock areal fracture intensity with semi-supervised learning using measurement-while-drilling data 认领 引用
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作者 Jun Wang Qian Fang +2 位作者 Weiguo He Guoli Zheng Jiayao Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4723-4741,共19页
The areal fracture intensity(P21)of the surrounding rock is a critical indicator for evaluating tunnel excavation stability.However,conventional methods often fail to capture geological conditions ahead of the tunnel ... The areal fracture intensity(P21)of the surrounding rock is a critical indicator for evaluating tunnel excavation stability.However,conventional methods often fail to capture geological conditions ahead of the tunnel face.With the development of measurement-while-drilling(MWD)technology,real-time acquisition of surrounding rock information has become feasible.Therefore,establishing a relationship between MWD data and the P21 is of significant importance.This study proposes a semisupervised deep learning framework that combines a multi-autoencoder unit training module(MAUT)with a multi-branch feature extraction network(MBEN).By integrating MWD,construction,and blasting data as dataset input,the proposed model is applied to the Yangjiawopu tunnel.The predicted P21 values exhibited a relative error of less than 10%,confirming the model's superior predictive performance and engineering applicability.Ablation studies demonstrate that both the MAUT module and feature fusion enhance model accuracy.The Shapley Additive exPlanations(SHAP)analysis highlights excavation length,water inflow,and mechanical specific energy(MSE)as key predictors.The model effectively leverages unlabeled data and provides robust geological insights,offering a data-driven approach for early fracture detection and risk assessment ahead of the tunnel face. 展开更多
关键词 Tunnel Areal fracture intensity Measurement while drilling(MWD) Semi-supervised learning
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Gearbox Fault Diagnosis under Varying Operating Conditions through Semi-Supervised Masked Contrastive Learning and Domain Adaptation 认领 引用
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作者 Zhixiang Huang Jun Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期448-470,共23页
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis... To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings. 展开更多
关键词 Gearbox variable working conditions fault diagnosis semi-supervised masked contrastive learning domain adaptation
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Deviation-Guided Attention for Semi-Supervised Anomaly Detection With Contrastive Regularisation 认领 引用
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作者 Guanglei Xie Xiaochang Hu +4 位作者 Yi Sun Wenzhuo Zhang Yafeng Bu Hao Fu Xin Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期66-82,共17页
Anomaly detection(AD)aims to identify abnormal patterns that deviate from normal behaviour,playing a critical role in applications such as industrial inspection,medical imaging and autonomous driving.However,AD often ... Anomaly detection(AD)aims to identify abnormal patterns that deviate from normal behaviour,playing a critical role in applications such as industrial inspection,medical imaging and autonomous driving.However,AD often faces a scarcity of labelled data.To address this challenge,we propose a novel semi-supervised anomaly detection method,DASAD(Deviation-Guided Attention for Semi-Supervised Anomaly Detection),which integrates deviation-guided attention with contrastive regularisation to reduce the unreliability of pseudo-labels.Specifically,a deviation-guided attention mechanism is designed to combine three types of deviations:latent embeddings,residual direction vectors and hierarchical reconstruction errors to capture anomaly specific cues effectively,thereby enhancing the credibility of pseudo-labels for unlabelled samples.Furthermore,a class-asymmetric contrastive loss is constructed to promote compact representations of normal instances while preserving the structural diversity of anomalies.Extensive experiments on 8 benchmark datasets demonstrate that DASAD consistently outperforms state-of-the-art methods and exhibits strong generalisation across 6 anomaly detection domains. 展开更多
关键词 anomaly detection deep learning semi-supervised learning
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AT-Net:A Semi-Supervised Framework for Asparagus Pathogenic Spore Detection under Complex Backgrounds 认领 引用
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作者 Jiajun Sun Shunshun Ji Chao Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第2期579-601,共23页
Asparagus stem blight is a devastating crop disease,and the early detection of its pathogenic spores is essential for effective disease control and prevention.However,spore detection is still hindered by complex backg... Asparagus stem blight is a devastating crop disease,and the early detection of its pathogenic spores is essential for effective disease control and prevention.However,spore detection is still hindered by complex backgrounds,small target sizes,and high annotation costs,which limit its practical application and widespread adoption.To address these issues,a semi-supervised spore detection framework is proposed for use under complex background conditions.Firstly,a difficulty perception scoring function is designed to quantify the detection difficulty of each image region.For regions with higher difficulty scores,a masking strategy is applied,while the remaining regions are adversarial augmentation is applied to encourage the model to learn fromchallenging areasmore effectively.Secondly,a Gaussian Mixture Model is employed to dynamically adjust the allocation threshold for pseudo-labels,thereby reducing the influence of unreliable supervision signals and enhancing the stability of semi-supervised learning.Finally,the Wasserstein distance is introduced for object localization refinement,offering a more robust positioning approach.Experimental results demonstrate that the proposed framework achieves 88.9% mAP50 and 60.7% mAP50-95,surpassing the baseline method by 4.2% and 4.6%,respectively,using only 10% of labeled data.In comparison with other state-of-the-art semi-supervised detection models,the proposed method exhibits superior detection accuracy and robustness.In conclusion,the framework not only offers an efficient and reliable solution for plant pathogen spore detection but also provides strong algorithmic support for real-time spore detection and early disease warning systems,with significant engineering application potential. 展开更多
关键词 Spore detection semi-supervised learning adaptive region enhancement Gaussian mixture model Wasserstein distance
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5G-SliceMatch:A Slice-Aware Semi-Supervised Learning Framework for Malicious Traffic Detection in 5G Networks 认领 引用
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作者 Jinha Kim Hwankuk Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1521-1555,共35页
The advent of 5th Generation(5G)mobile networks has introduced Network Slicing as a core mechanism for supporting heterogeneous vertical services—such as enhanced Mobile Broadband(eMBB),Ultra-Reliable LowLatency Comm... The advent of 5th Generation(5G)mobile networks has introduced Network Slicing as a core mechanism for supporting heterogeneous vertical services—such as enhanced Mobile Broadband(eMBB),Ultra-Reliable LowLatency Communication(URLLC),and massive Machine-Type Communication(mMTC)over a shared physical infrastructure,thereby significantly expanding the attack surface at the User Plane Function(UPF).Securing this multi-slice environment requires intrusion detection systems that can simultaneously accommodate the statistical heterogeneity of per-slice traffic and the stringent Quality of Service(QoS)constraints of real-time slices,yet the practical cost of obtaining high-quality labeled traffic in operational 5G cores remains prohibitive.This study proposes 5G-SliceMatch,a Slice-Aware Semi-Supervised Learning framework,to address the challenge of malicious traffic detection in 5G network slicing environments under severe label scarcity.Traditional intrusion detection systems fail to account for the heterogeneous traffic characteristics of different 5G slices,leading to significant performance degradation in resource-constrained slices like URLLC.To overcome this,5G-SliceMatch integrates a Slice-Aware Teacher Model with dedicated classification heads,slice-specific differential Feature Masking,and a progressive Self-Training strategy.Experimental results on the 5G-SliciNdd dataset demonstrate that 5G-SliceMatch consistently outperforms state-of-the-art baselines.Specifically,in an extreme scenario with only 1%of labeled data,5G-SliceMatch achieved an F1-Score of 0.9393,outperforming XGBoost by 2.82%.Moreover,it achieves 98.1%of the performance of a fully supervised XGBoost(100%labels)using only 5%of the data,effectively reducing the manual labeling workload by 95%.This work proves that 5G-SliceMatch-by integrating slice-aware architectural design with efficient semi-supervised learning is critical for achieving high-resolution security visibility while satisfying the stringent QoS requirements of complex 5G network environments. 展开更多
关键词 5G network slicing network IDS semi-supervised learning
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PL-HLANet:a semi-supervised approach for tunnel boring machine disc cutter wear prediction 认领 引用
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作者 Zhaoyang LI Wei TANG +3 位作者 Xinyuan WANG Huxiu XU Huayong YANG Jun ZOU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第4期317-333,I0001-I0007,I0048,共17页
Unanticipated wear of tunnel boring machine(TBM)disc cutters is a critical factor causing project delays and cost overruns in tunneling engineering.Accurate,real-time prediction of the cutter’s wear state is therefor... Unanticipated wear of tunnel boring machine(TBM)disc cutters is a critical factor causing project delays and cost overruns in tunneling engineering.Accurate,real-time prediction of the cutter’s wear state is therefore essential for enabling predictive maintenance.Data-driven methods,particularly deep learning,have shown promise for this task,but their performance is constrained by the scarcity of high-quality labeled data in practical industrial settings.To address this challenge,we propose a novel,decoupled semi-supervised framework called PL-HLANet.The first component of this framework is a multi-view pseudolabeling(PL)module,which mines high-confidence supervisory signals from massive unlabeled data by leveraging heterogeneous views derived from feature engineering and diverse model architectures;it is followed by a consistency check to ensure label quality.This process effectively augments the training set while correcting for sampling bias.Subsequently,a specialized hierarchical hybrid attention network(HLANet)is used to make predictions.The HLANet organically integrates a temporal convolutional network(TCN)for local feature extraction,a bidirectional long short-term memory(Bi-LSTM)network for capturing temporal dynamics,and a custom attention mechanism for focusing on critical information.Experiments on a realworld tunneling dataset show that PL-HLANet significantly outperforms both supervised and mainstream semi-supervised baselines,such as the Mean Teacher and FixMatch.The framework’s effectiveness is further substantiated by validations of its architectural design and data-driven selection of hyperparameters.Moreover,PL-HLANet has a high inference speed,showcasing its practicality for real-world scenarios.Our work provides an effective solution for machining equipment monitoring in datascarce industrial environments. 展开更多
关键词 Tunnel boring machine(TBM) Cutter wear Semi-supervised learning Pseudo-labeling(PL) Temporal convolutional network Bidirectional long short-term memory(Bi-LSTM) Attention mechanism
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RoCoNet:Rotational Contrastive Network for Semi-Supervised Cervical Cell Image Object Detection 认领 引用
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作者 HUANG Qiubo GONG Runze CHEN Dehua 《Journal of Donghua University(English Edition)》 CAS 2026年第2期82-93,共12页
A semi-supervised learning framework integrating rotational invariance,contrastive learning,and adaptive hybrid thresholds,named rotational contrastive network(RoCoNet),is proposed to enhance the applicability of semi... A semi-supervised learning framework integrating rotational invariance,contrastive learning,and adaptive hybrid thresholds,named rotational contrastive network(RoCoNet),is proposed to enhance the applicability of semisupervised learning for medical cell datasets.Due to the unique sampling approach of cell datasets,input images often contain uncertain rotation angles,which render traditional convolution kernels ineffective in existing semi-supervised detectors.To address this challenge,rotational attention convolution is introduced,offering robustness to rotational transformations.Additionally,cross-feature contrastive loss is proposed to improve upon the contrastive loss used in supervised learning,tackling issues of poor classification performance caused by cell overlap and clustering.An adaptive hybrid threshold is also introduced to stabilize pseudo-label generation during early training.A global threshold,computed by using Gaussian mixture models(GMMs),is applied to refine the local threshold,which helps balance the quantity and quality of pseudo-labels.Experiments on the ThinPrep cytology test(TCT)dataset for cervical cytopathology show that RoCoNet achieves a mean average precision(mAP)of 31.6%with only 10%labeled data,outperforming the baseline method by 8.4%in mAP. 展开更多
关键词 semi-supervised learning rotational invariance contrastive learning object detection cervical cell
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Enhanced battery life prediction with reduced data demand via semi-supervised representation learning 认领 引用 被引量：2
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作者 Liang Ma Jinpeng Tian +2 位作者 Tieling Zhang Qinghua Guo Chi Yung Chung 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2025年第2期524-534,I0011,共11页
Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlo... Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices. 展开更多
关键词 Lithium-ion batteries Battery degradation Remaining useful life Semi-supervised learning
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Semi-supervised method for tunnel blasting quality prediction using measurement while drilling data 认领 引用 被引量：1
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作者 Hengxiang Jin Qian Fang +3 位作者 Jun Wang Jiayao Chen Gan Wang Guoli Zheng 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第5期2633-2649,共17页
Predicting blasting quality during tunnel construction holds practical significance.In this study,a new semi-supervised learning method using convolutional variational autoencoder(CVAE)and deep neural network(DNN)is p... Predicting blasting quality during tunnel construction holds practical significance.In this study,a new semi-supervised learning method using convolutional variational autoencoder(CVAE)and deep neural network(DNN)is proposed for the prediction of blasting quality grades.Tunnel blasting quality can be measured by over/under excavation.The occurrence of over/under excavation is influenced by three factors:geological conditions,blasting parameters,and tunnel geometric dimensions.The proposed method reflects the geological conditions through measurements while drilling and utilizes blasting parameters,tunnel geometric dimensions,and tunnel depth as input variables to achieve tunnel blasting quality grades prediction.Furthermore,the model is optimized by considering the influence of surrounding rock mass features on the predicted positions.The results demonstrate that the proposed method outperforms other commonly used machine learning and deep learning algorithms in extracting over/under excavation feature information and achieving blasting quality prediction. 展开更多
关键词 Tunnel blasting quality Over/under excavation Semi-supervised learning Measurement while drilling(MWD)
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Multi-Consistency Training for Semi-Supervised Medical Image Segmentation 认领 引用 被引量：1
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作者 WU Changxue ZHANG Wenxi +1 位作者 HAN Jiaozhi WANG Hongyu 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第4期800-814,共15页
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
关键词 semi-supervised learning(SSL) multi-consistency training(MCT) medical image segmentation intermediate feature perturbation
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Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation 认领 引用
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作者 Hengyang Liu Yang Yuan +2 位作者 Pengcheng Ren Chengyun Song Fen Luo 《Computers, Materials & Continua》 SCIE EI 2025年第1期543-560,共18页
Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)t... Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset. 展开更多
关键词 Semi-supervised medical image segmentation contrastive learning stochastic augmented
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Semi-supervised cardiac magnetic resonance image segmentation based on domain generalization 认领 引用
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作者 SHAO Hong HOU Jinyang CUI Wencheng 《High Technology Letters》 EI CAS 2025年第1期41-52,共12页
In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when fa... In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when faced with testing scenarios from unknown domains.To address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of pseudo-labels.Additionally,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain perspective.This papers method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effectiveness.Comparative analyses against various methods highlight the out-standing performance of this papers approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data. 展开更多
关键词 semi-supervised domain generalization(DG) cardiac magnetic resonance image segmentation
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Semi-supervised least squares support vector machine algorithm:application to offshore oil reservoir 认领 引用 被引量：6
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作者 罗伟平 李洪奇 石宁 《Applied Geophysics》 SCIE CSCD 2016年第2期406-415,421,共10页
At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict th... At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine （LSSVM） algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine （SLSSVM） model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor （SKNN） method. The new semi- supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area. 展开更多
关键词 Semi-supervised learning least squares support vector machine seismic attributes reservoir prediction
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Semi-Supervised Medical Image Classification Based on Sample Intrinsic Similarity Using Canonical Correlation Analysis 认领 引用
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作者 Kun Liu Chen Bao Sidong Liu 《Computers, Materials & Continua》 SCIE EI 2025年第3期4451-4468,共18页
Large amounts of labeled data are usually needed for training deep neural networks in medical image studies,particularly in medical image classification.However,in the field of semi-supervised medical image analysis,l... Large amounts of labeled data are usually needed for training deep neural networks in medical image studies,particularly in medical image classification.However,in the field of semi-supervised medical image analysis,labeled data is very scarce due to patient privacy concerns.For researchers,obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding.In addition,skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions.In this paper,we propose a model called Coalition Sample Relation Consistency(CSRC),a consistency-based method that leverages Canonical Correlation Analysis(CCA)to capture the intrinsic relationships between samples.Considering that traditional consistency-based models only focus on the consistency of prediction,we additionally explore the similarity between features by using CCA.We enforce feature relation consistency based on traditional models,encouraging the model to learn more meaningful information from unlabeled data.Finally,considering that cross-entropy loss is not as suitable as the supervised loss when studying with imbalanced datasets(i.e.,ISIC 2017 and ISIC 2018),we improve the supervised loss to achieve better classification accuracy.Our study shows that this model performs better than many semi-supervised methods. 展开更多
关键词 Semi-supervised learning skin lesion classification sample relation consistency class imbalanced
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Improved Semi-supervised Clustering Algorithm Based on Affinity Propagation 认领 引用 被引量：3
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作者 金冉 刘瑞娟 +1 位作者 李晔锋 寇春海 《Journal of Donghua University(English Edition)》 EI CAS 2015年第1期125-131,共7页
A clustering algorithm for semi-supervised affinity propagation based on layered combination is proposed in this paper in light of existing flaws. To improve accuracy of the algorithm,it introduces the idea of layered... A clustering algorithm for semi-supervised affinity propagation based on layered combination is proposed in this paper in light of existing flaws. To improve accuracy of the algorithm,it introduces the idea of layered combination, divides an affinity propagation clustering( APC) process into several hierarchies evenly,draws samples from data of each hierarchy according to weight,and executes semi-supervised learning through construction of pairwise constraints and use of submanifold label mapping,weighting and combining clustering results of all hierarchies by combined promotion. It is shown by theoretical analysis and experimental result that clustering accuracy and computation complexity of the semi-supervised affinity propagation clustering algorithm based on layered combination( SAP-LC algorithm) have been greatly improved. 展开更多
关键词 semi-supervised clustering affinity propagation(AP) layered combination computation complexity combined promotion
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A Semi-Supervised WLAN Indoor Localization Method Based on l1-Graph Algorithm 认领 引用 被引量：1
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作者 Liye Zhang Lin Ma Yubin Xu 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2015年第4期55-61,共7页
For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be colle... For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase. 展开更多
关键词 indoor location estimation l1-graph algorithm semi-supervised learning wireless local area networks(WLAN)
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