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Competitive dual-students using bi-level contrastive learning for semi-supervised medical image segmentation

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 144卷

作者： Hu, Gang Zhao, Feng Houssein, Essam H. Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Minia Univ Fac Comp & Informat Al Minya Egypt

semi-supervised image segmentation aims to train the neural network with a small number of labeled images and a large number of unlabeled images, which helps to alleviate the burden of having less manually labeled medical data. However, the Mean-Teacher (MT) model, a benchmark method for semi-supervised medical segmentation, leads to a performance bottleneck as its student model eventually converges to the teacher model. In addition, existing segmentation methods treat all pixels equally and underestimate the importance of indistinguishable and underrepresented pixels, failing to mine the potential information in these regions effectively. To address the above issues, this paper proposes a Competitive Dual-Student (CDS) incorporating bi-level contrastive learning. First, an additional competitive dual-student model is added to the MT model and promoting knowledge sharing and complementarity among networks. Competitive instruction by the teacher through feature information exchange and positive comparisons reduces the accumulation of biased knowledge in the model. It stimulates the potential for further optimization of the model as a whole. Furthermore, a bi-level contrastive learning is designed. The high-level contrastive learning encourages competitive dual students to learn high-quality features from each other by constructing reliability constraints. The low-level contrastive achieved deep mining and accurate processing of local edge features by introducing class prototypes of high-quality features for teacher networks. Finally, the comprehensive experimental results on left atrium, brain tumor segmentation 2019 and automated cardiac diagnosis challenge datasets indicate that the segmentation performance of the proposed CDS outperforms the state-of-the-art compared methods. Code is released at https://***/FengZhao2001/CDS.

关键词： Neural network semi-supervised image segmentation Competitive dual-student Bi-level contrastive learning Class prototypes

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Co-evidential fusion with information volume for semi-supervised medical image segmentation

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PATTERN RECOGNITION 2025年 166卷

作者： He, Yuanpeng Li, Lijian Zhan, Tianxiang Pun, Chi-Man Jiao, Wenpin Jin, Zhi Peking Univ Key Lab High Confidence Software Technol Minist Educ Beijing 100871 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China Univ Macau Dept Comp & Informat Sci Zhuhai 999078 Macao Peoples R China

Although existing semi-supervised image segmentation methods have achieved good performance, they cannot effectively utilize multiple sources of voxel-level uncertainty for targeted learning. Therefore, we propose two main improvements. First, we introduce a novel pignistic co-evidential fusion strategy using generalized evidential deep learning, extended by traditional D-S evidence theory, to obtain a more precise uncertainty measure for each voxel in medical samples. This assists the model in learning mixed labeled information and establishing semantic associations between labeled and unlabeled data. Second, we introduce the concept of information volume of mass function (IVUM) to evaluate the constructed evidence, implementing two evidential learning schemes. One optimizes evidential deep learning by combining the information volume of the mass function with original uncertainty measures. The other integrates the learning pattern based on the co-evidential fusion strategy, using IVUM to design a new optimization objective. Experiments on four datasets demonstrate the competitive performance of our method.

关键词： semi-supervised image segmentation Co-Evidential fusion Information volume

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semi-supervised image segmentation COMBINING SSFCM AND RANDOM WALKS

SEMI-SUPERVISED IMAGE SEGMENTATION COMBINING SSFCM AND RANDO...

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2nd IEEE International Conference on Cloud Computing and Intelligent Systems (CCIS)

作者： Chen, Shengguo Sun, Zhengxing Zhou, Jie Li, Yi Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China

ISBN: (纸本)9781467318556;9781467318570

We present a semi-supervised image segmentation algorithm to segment the noisy image that includes a large amount of objects with the same color features. It models the image's color feature through SSFCM based labeled data, and then it defines a reliability function based upon the membership calculated by SSFCM, and the pixels are classified as two types that are considered as labeled and unlabeled pixels of Random Walks, at last it performs Random Walks to produce the final segmentation. The experimental results show the effectiveness of our algorithm. It not only reduces the noise sensitivity of SSFCM but also avoids cumbersome operations that the user labels the seed points of all objects for Random Walks.

关键词： semi-supervised image segmentation SSFCM Random walks

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Graph Convolutional Networks for semi-supervised image segmentation

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IEEE ACCESS 2022年 10卷 104144-104155页

作者： Fabijanska, Anna Lodz Univ Technol Inst Appl Comp Sci PL-90537 Lodz Poland

The problem of image segmentation is one of the most significant ones in computer vision. Recently, deep-learning methods have dominated state-of-the-art solutions that automatically or interactively divide an image into subregions. However, the limitation of deep-learning approaches is that they require a substantial amount of training data, which is costly to prepare. An alternative solution is semi-supervised image segmentation. It requires rough denotations to define constraints that are next generalized to precisely delimit relevant image regions without using train examples. Among semi-supervised strategies for image segmentation, the leading are graph-based techniques that define image segmentation as a result of pixel or region affinity graph partitioning. This paper revisits the problem of graph-based image segmentation. It approaches the problem as semi-supervised node classification in the SLIC superpixels region adjacency graph using a graph convolutional network (GCN). The performance of both spectral and spatial graph convolution operators is considered, represented by Chebyshev convolution operator and GraphSAGE respectively. The results of the proposed method applied to binary and multi-label segmentation are presented, numerically assessed, and analyzed. In its best variant, the proposed method scored the average DICE of 0.86 in the binary segmentation task and 0.79 in the multi-label segmentation task. Comparison with state-of-the-art graph-based methods, including Random Walker and GrabCut, shows that graph convolutional networks can represent an attractive alternative to the existing solutions to graph-based semi-supervised image segmentation.

关键词： image segmentation Clustering methods Convolutional neural networks image edge detection Graph neural networks Laplace equations semi-supervised learning GCN graph convolutional networks graph node clustering region adjacency graph semi-supervised image segmentation

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semi-supervised image segmentation COMBINING SSFCM AND RANDOM WALKS

SEMI-SUPERVISED IMAGE SEGMENTATION COMBINING SSFCM AND RANDO...

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

作者： Shengguo Chen Zhengxing Sun Jie Zhou Yi Li State Key Laboratory for Novel Software Technology Nanjing University

关键词： semi-supervised image segmentation SSFCM Random walks

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Self Pseudo Entropy Knowledge Distillation for semi-supervised Semantic segmentation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第8期34卷 7359-7372页

作者： Lu, Xiaoqiang Jiao, Licheng Li, Lingling Liu, Fang Liu, Xu Yang, Shuyuan Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist EducInt Res Ctr Intelligent Percept & Comp Xian 710071 Shaanxi Peoples R China

Recently, semi-supervised semantic segmentation methods based on weak-to-strong consistency learning have achieved the most advanced performance. The key to such a technique lies in strong perturbations and multi-objective co-training. However, CutMix, the most commonly used data augmentation in this field, limits the strength of perturbations as it only focuses on single random local context. Besides, complex optimization targets also reduce computational efficiency. In this work, we propose an efficient consistency learning based framework. Specifically, a novel unsupervised data augmentation strategy, EntropyMix, is present for semi-supervised semantic segmentation. Patches of unlabeled data from multi-view augmentations are combined into new training samples based on their prediction entropy, which provides more informative and powerful perturbations for consistency regularization and impels the model to focus on cross-view local context. On this basis, we further propose Self Pseudo Entropy knowledgE Distillation (SPEED) to learn global pixel relations from multi- and cross-view perturbations by optimizing a linear combination of feature- and logit-level distillation loss, enhancing model performance without additional auxiliary segmentation heads or a complex pre-trained teacher model. The collocation of the two ideas above is a plug-and-play technique without additional modification. Extensive experimental results on PASCAL VOC and Cityscapes datasets under various training settings demonstrate the superiority of the proposed data augmentation strategy and self-distillation loss, achieving new state-of-the-art performance. Remarkably, our method reaches mIoU of 75.16% using only 0.87% labeled data on PASCAL VOC and mIoU of 76.98% using only 6.25% labeled data on Cityscapes. The code is available at https://***/xiaoqiang-lu/SPEED.

关键词： Perturbation methods Training Data augmentation Semantic segmentation Entropy Optimization Semantics semi-supervised image segmentation data augmentation self knowledge distillation

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Self-Ensembling Co-Training Framework for semi-supervised COVID-19 CT segmentation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第11期25卷 4140-4151页

作者： Li, Caizi Dong, Li Dou, Qi Lin, Fan Zhang, Kebao Feng, Zuxin Si, Weixin Deng, Xuesong Deng, Zhe Heng, Pheng-Ann Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Zhijiang Peoples Hosp Dept Radiol Yichang 443200 Hubei Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Shenzhen Univ Shenzhen Peoples Hosp 2 Affiliated Hosp 1 Hlth Sci CtrDept Radiol Shenzhen 518035 Guangdong Peoples R China Shenzhen Univ Shenzhen Peoples Hosp 2 Affiliated Hosp 1 Hlth Sci CtrDept Emergency Med Shenzhen 518035 Guangdong Peoples R China Peking Univ ShenZhen Hosp Dept Emergency Med Shenzhen 518036 Guangdong Peoples R China Shenzhen Univ Affiliated Hosp 1 Shenzhen Peoples Hosp 2 Hlth Sci CtrDept Hepatobiliary Surg Shenzhen 518035 Guangdong Peoples R China

The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. segmentation of COVID lesions from computed tomography (CT) scans is of great importance for supervising disease progression and further clinical treatment. As labeling COVID-19 CT scans is labor-intensive and time-consuming, it is essential to develop a segmentation method based on limited labeled data to conduct this task. In this paper, we propose a self-ensembled co-training framework, which is trained by limited labeled data and large-scale unlabeled data, to automatically extract COVID lesions from CT scans. Specifically, to enrich the diversity of unsupervised information, we build a co-training framework consisting of two collaborative models, in which the two models teach each other during training by using their respective predicted pseudo-labels of unlabeled data. Moreover, to alleviate the adverse impacts of noisy pseudo-labels for each model, we propose a self-ensembling strategy to perform consistency regularization for the up-to-date predictions of unlabeled data, in which the predictions of unlabeled data are gradually ensembled via moving average at the end of every training epoch. We evaluate our framework on a COVID-19 dataset containing 103 CT scans. Experimental results show that our proposed method achieves better performance in the case of only 4 labeled CT scans compared to the state-of-the-art semi-supervised segmentation networks.

关键词： COVID-19 Computed tomography Lesions image segmentation Data models Predictive models Training COVID-19 CT segmentation semi-supervised image segmentation self-ensembling model co-training

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A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 65卷 116-126页

作者： Cordeiro, Filipe R. Santos, Wellington P. Silva-Filho, Abel G. Univ Fed Pernambuco Informat Ctr Recife PE Brazil Univ Fed Pernambuco Dept Biomed Engn Recife PE Brazil

According to the World Health Organization, breast cancer is the most common form of cancer in women. It is the second leading cause of death among women round the world, becoming the most fatal form of cancer. Despite the existence of several imaging techniques useful to aid at the diagnosis of breast cancer, x-ray mammography is still the most used and effective imaging technology. Consequently, mammographic image segmentation is a fundamental task to support image analysis and diagnosis, taking into account shape analysis of mammary lesions and their borders. However, mammogram segmentation is a very hard process, once it is highly dependent on the types of mammary tissues. The GrowCut algorithm is a relatively new method to perform general image segmentation based on the selection of just a few points inside and outside the region of interest, reaching good results at difficult segmentation cases when these points are correctly selected. In this work we present a new semi-supervised segmentation algorithm based on the modification of the GrowCut algorithm to perform automatic mammographic image segmentation once a region of interest is selected by a specialist. In our proposal, we used fuzzy Gaussian membership functions to modify the evolution rule of the original GrowCut algorithm, in order to estimate the uncertainty of a pixel being object or background. The main impact of the proposed method is the significant reduction of expert effort in the initialization of seed points of GrowCut to perform accurate segmentation, once it removes the need of selection of background seeds. Furthermore, the proposed method is robust to wrong seed positioning and can be extended to other seed based techniques. These characteristics have impact on expert and intelligent systems, once it helps to develop a segmentation method with lower required specialist knowledge, being robust and as efficient as state of the art techniques. We also constructed an automatic point selection

关键词： Breast cancer Mammographic image analysis semi-supervised image segmentation GrowCut algorithm Fuzzy segmentation Simulated annealing

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Vision-based preharvest yield mapping for apple orchards

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2019年 164卷 104897-000页

作者： Roy, Pravakar Kislay, Abhijeet Plonski, Patrick A. Luby, James Isler, Volkan Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Univ Minnesota Dept Hort Sci Minneapolis MN 55455 USA

platform independent and does not require any specific lighting conditions. Our main technical contributions are (1) a semi-supervised clustering algorithm that utilizes colors to identify apples and (2) an unsupervised clustering method that utilizes spatial properties to estimate fruit counts from apple clusters having arbitrarily complex geometry. Additionally, we utilize camera motion to merge the counts across multiple views. We verified the performance of our algorithms by conducting multiple field trials. Results indicate that the detection method achieves F-l-measure .95-.97 for multiple color varieties and lighting conditions. The counting method achieves an accuracy of 89-98%. Additionally, we report merged fruit counts from both sides of the tree rows. Our yield estimation method achieves an overall accuracy of 91.98-94.81% across different datasets.

关键词： Yield estimation Apple detection Apple counting semi-supervised image segmentation Machine vision Clustering

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