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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Turek, Javier S. Willke, Theodore L. Chen, Po-Hsuan Ramadge, Peter J. Intel Labs Parallel Comp Lab Hillsboro OR 97124 USA Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)9781509041176

Practical limitations on the duration of individual fMRI scans have led neuroscientist to consider the aggregation of data from multiple subjects. Differences in anatomical structures and functional topographies of brains require aligning data across subjects. Existing functional alignment methods serve as a preprocessing step that allows subsequent statistical methods to learn from the aggregated multi-subject data. Despite their success, current alignment methods do not leverage the labeled data used in the subsequent methods. In this work we propose a semi-supervised scheme that simultaneously learns the alignment and performs the analysis. We derive a specific instance of the scheme using the Shared Response Model for alignment and Multinomial Logistic Regression for classification. In our experiments this method improves the average classification accuracy from 65.5% to 68.5%, and from 5.3% to 6.1% over the independently-trained methods. Furthermore, our method achieves similar prediction with almost half the samples used for alignment.

关键词： fMRI functional alignment shared response model semi-supervised method

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A semi-supervised method for Efficient Construction of Statistical Spoken Language Understanding Resources

A Semi-supervised Method for Efficient Construction of Stati...

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Interspeech Conference 2007

作者： Kim, Seokhwan Jeong, Minwoo Lee, Gary Geunbae Pohang Univ Sci & Technol Dept Comp Sci & Engn Pohang South Korea

ISBN: (纸本)9781605603162

We present a semi-supervised framework to construct spoken language understanding resources with very low cost. We generate context patterns with a few seed entities and a large amount of unlabeled utterances. Using these context patterns, we extract new entities from the unlabeled utterances. The extracted entities are appended to the seed entities, and we can obtain the extended entity list by repeating these steps. Our method is based on an utterance alignment algorithm which is a variant of the biological sequence alignment algorithm. Using this method, we can obtain precise entity lists with high coverage, which is of help to reduce the cost of building resources for statistical spoken language understanding systems.

关键词： semi-supervised method spoken language understanding

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A semi-supervised method for multi-subject FMRI functional alignment

A semi-supervised method for multi-subject FMRI functional a...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Javier S. Turek Theodore L. Willke Po-Hsuan Chen Peter J. Ramadge Parallel Computing Lab Intel Labs Hillsboro Oregon USA Department of Electrical Engineering Princeton University New Jersey USA

ISBN: (纸本)9781509041183

关键词： fMRI functional alignment shared response model semi-supervised method

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A semi-supervised prototypical network for prostate lesion segmentation from multimodality MRI

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PHYSICS IN MEDICINE AND BIOLOGY 2025年第8期70卷 2025 Mar 17页

作者： Yan, Wen Hu, Yipeng Yang, Qianye Fu, Yunguan Syer, Tom Min, Zhe Punwani, Shonit Emberton, Mark Barratt, Dean C. Cho, Carmen C. M. Chiu, Bernard City Univ Hong Kong Dept Elect Engn 83 Tat Chee Ave Hong Kong Peoples R China Wilfrid Laurier Univ Dept Phys & Comp Sci 75 Univ Ave West Waterloo ON N2L 3C5 Canada UCL UCL Hawkes Inst Dept Med Phys & Biomed Engn Gower St London WC1E 6BT England UCL Ctr Med Imaging Div Med Foley St London W1W 7TS England UCL Div Surg & Intervent Sci Gower St London WC1E 6BT England Prince Wales Hosp Dept Imaging & Intervent Radiol Shatin 30-32 Ngan Shing St Hong Kong Peoples R China InstaDeep Dept BioAI 5 Merchant Sq London W2 1AY England

Objective. Prostate lesion segmentation from multiparametric magnetic resonance images is particularly challenging due to the limited availability of labeled data. This scarcity of annotated images makes it difficult for supervised models to learn the complex features necessary for accurate lesion detection and segmentation. Approach. We proposed a novel semi-supervised algorithm that embeds prototype learning into mean-teacher (MT) training to improve the feature representation for unlabeled data. In this method, pseudo-labels generated by the teacher network simultaneously serve as supervision for unlabeled prototype-based segmentation. By enabling prototype segmentation to operate across labeled and unlabeled data, the network enriches the pool of "lesion representative prototypes", and allows prototypes to flow bidirectionally-from support-to-query and query-to-support paths. This intersected, bidirectional information flow strengthens the model's generalization ability. This approach is distinct from the MT algorithm as it involves few-shot training and differs from prototypical learning for adopting unlabeled data for training. Main results. This study evaluated multiple datasets with 767 patients from three different institutions, including the publicly available PROSTATEx/PROSTATEx2 datasets as the holdout institute for reproducibility. The experimental results showed that the proposed algorithm outperformed state-of-the-art semi-supervised methods with limited labeled data, observing an improvement in Dice similarity coefficient with increasing labeled data, ranging from 0.04 to 0.09. Significance. Our method shows promise in improving segmentation outcomes with limited labeled data and potentially aiding clinicians in making informed patient treatment and management decisions88The algorithm implementation has been made available on GitHub via git@***:yanwenCi/***..

关键词： prototypical algorithm semi-supervised method prostate lesion segmentation

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A semi-supervised inattention detection method using biological signal

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ANNALS OF OPERATIONS RESEARCH 2017年第1期258卷 59-78页

作者： Choi, Yerim Park, Jonghun Shin, Dongmin Kyonggi Univ Dept Ind & Management Engn Suwon 16227 Gyeonggi South Korea Seoul Natl Univ Dept Ind Engn Seoul 151744 South Korea Seoul Natl Univ Inst Ind Syst Innovat Seoul 151744 South Korea Hanyang Univ Dept Ind & Management Engn Ansan 425791 Gyeonggi South Korea

Recently, operations research methods have been utilized for biological data analysis as a huge amount of biological data becomes available. One of popular applications of the data analysis is inattention detection of operators in human-machine interaction systems using electroencephalography (EEG) signal. Most of the previous studies on the inattention detection employed supervised learning approaches, but their results have potential bias since they rely on imperfect assumptions for the acquisition of mental state labels, attention and inattention, due to the absence of the standardized measure for the mental states. Instead, we consider unsupervised learning approach, where no labeled data is required. In order to address the low performance of unsupervised learning approaches, attention duration for which an operator sustains his/her attention from the beginning of performing a task and relevance levels between four attributes of EEG signal and mental states are exploited. In this regard, we propose a semi-supervised inattention detection method (SID), in which attention duration and attributes-weights of EEG signal are respectively utilized as a small portion of labeled data for semi-supervised learning and weights for similarity calculation. Specifically, cumulative sum algorithm is used for the determination of the attention duration, and constrained attributes-weighting clustering algorithm is used for the estimation of attributes-weights. From experiments using real-world dataset, SID outperformed the compared methods, and it is expected that the adoption of SID will contribute to the enhancement of the operators' safety.

关键词： Inattention detection Biological signal semi-supervised method Cumulative sum algorithm Constrained attributes-weighting clustering algorithm

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semi-supervised MEDICAL IMAGE SEGMENTATION method BASED ON CROSS-PSEUDO LABELING LEVERAGING STRONG AND WEAK DATA AUGMENTATION STRATEGIES 21

SEMI-SUPERVISED MEDICAL IMAGE SEGMENTATION METHOD BASED ON C...

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21st IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Chen, Yifei Zhang, Chenyan Ke, Yifan Huang, Yiyu Dai, Xuezhou Qin, Feiwei Zhang, Yongquan Zhang, Xiaodong Wang, Changmiao Hangzhou Dianzi Univ Hangzhou Peoples R China Zhejiang Univ Finance & Econ Hangzhou Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Shenzhen Childrens Hosp Shenzhen Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China

ISBN: (纸本)9798350313345;9798350313338

Traditional supervised learning methods have historically encountered certain constraints in medical image segmentation due to the challenging collection process, high labeling cost, low signal-to-noise ratio, and complex features characterizing biomedical images. This paper proposes a semi-supervised model, DFCPS, which innovatively incorporates the Fixmatch concept. This significantly enhances the model's performance and generalizability through data augmentation processing, employing varied strategies for unlabeled data. Concurrently, the model design gives appropriate emphasis to the generation, filtration, and refinement processes of pseudo-labels. The novel concept of cross-pseudo-supervision is introduced, integrating consistency learning with self-training. This enables the model to fully leverage pseudo-labels from multiple perspectives, thereby enhancing training diversity. The DFCPS model is compared with both baseline and advanced models using the publicly accessible Kvasir-SEG dataset. Across all four subdivisions containing different proportions of unlabeled data, our model consistently exhibits superior performance. Our source code is available at https://***/JustlfC03/DFCPS.

关键词： Medical image segmentation semi-supervised method Cross Pseudo Supervision Data augmentation strategy

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Learning to select pseudo labels: a semi-supervisedmethod for named entity recognition

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Frontiers of Information Technology & Electronic Engineering 2020年第6期21卷 903-916页

作者： Zhen-zhen LI Da-wei FENG Dong-sheng LI Xi-cheng LU College of Computer National University of Defense TechnologyChangsha 410073China

Deep learning models have achieved state-of-the-art performance in named entity recognition(NER);the good performance,however,relies heavily on substantial amounts of labeled *** some specific areas such as medical,financial,and military domains,labeled data is very scarce,while unlabeled data is readily *** studies have used unlabeled data to enrich word representations,but a large amount of entity information in unlabeled data is neglected,which may be beneficial to the NER *** this study,we propose a semi-supervised method for NER tasks,which learns to create high-quality labeled data by applying a pre-trained module to filter out erroneous pseudo *** labels are automatically generated for unlabeled data and used as if they were true *** semi-supervised framework includes three steps:constructing an optimal single neural model for a specific NER task,learning a module that evaluates pseudo labels,and creating new labeled data and improving the NER model *** results on two English NER tasks and one Chinese clinical NER task demonstrate that our method further improves the performance of the best single neural *** when we use only pre-trained static word embeddings and do not rely on any external knowledge,our method achieves comparable performance to those state-of-the-art models on the CoNLL-2003 and OntoNotes 5.0 English NER tasks.

关键词： Named entity recognition Unlabeled data Deep learning semi-supervised method

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A joint-L_2,1-norm-constraint-based semi-supervised feature extraction for RNA-Seq data analysis

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NEUROCOMPUTING 2017年 228卷 263-269页

作者： Liu, Jin-Xing Wang, Dong Gao, Ying-Lian Zheng, Chun-Hou Shang, Jun-Liang Liu, Feng Xu, Yong Qufu Normal Univ Sch Informat Sci & Engn Rizhao Peoples R China Harbin Inst Technol Shenzhen Grad Sch Biocomp Res Ctr Shenzhen Peoples R China Qufu Normal Univ Lib Qufu Normal Univ Rizhao Peoples R China Anhui Univ Sch Mech Engn & Automat Hefei Peoples R China Shenzhen Univ Sch Comp Sci & Software Engn Shenzhen Peoples R China

It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. In this paper, we proposed a novel method, joint-L-2,L-1-norm-constraint-based semi-supervised feature extraction (L21SFE), to analyze RNA-Seq data. Our scheme was shown as follows. Firstly, we constructed a graph Laplacian matrix and refined it by using the labeled samples. Our graph construction method can make full use of a large number of unlabelled samples. Secondly, we found semi-supervised optimal maps by solving a generalized eigenvalue problem. Thirdly, we solved an optimal problem via the joint-L2,1-norm constraint to obtain a projection matrix. It can diminish the impact of noises and outliers by using the L-2,L-1-norm constraint and produce more precise results. Finally, we identified differentially expressed genes based on the projection matrix. The results on simulation and real RNA-Seq data sets demonstrated the feasibility and effectiveness of our method.

关键词： Feature extraction L-2,L-1-norm constraint Spectral regression semi-supervised method RNA-Seq data analysis

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Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第10期36卷 3535-3556页

作者： Fang, Zhice Wang, Yi Duan, Hexiang Niu, Ruiqing Peng, Ling China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Peoples R China China Inst Geoenvironm Monitoring Beijing 100081 Peoples R China

Landslide susceptibility prediction is a key step in preventing and managing landslide hazards. As a classical supervised non-parametric machine learning model, support vector machine (SVM) has been widely used in landslide susceptibility prediction in recent years. However, most studies focus on the application of general SVM methods, or compare SVMs as benchmark methods. SVMs with different kernel functions are rarely used in this field. In this study, we apply the general SVM and its popular variants (i.e., multiple kernel learning, infinite ensemble SVM and semi-supervised SVM) to predict landslide susceptibility, and compare their prediction performance. The experimental results show that the Laplacian-SVM has the highest prediction performance (AUC = 0.8815) among SVM-based methods. SVMs with RBF kernel can achieve higher performance than SVMs with linear kernel, indicating that RBF kernel is more suitable for solving susceptibility prediction problems. Furthermore, SVM-based methods have higher sensitivity (0.8543-0.9288) than deep learning methods (0.8237-0.8271), which proves the advantage of SVMs in finding potential landslide areas.

关键词： Landslide susceptibility mapping Support vector machine Multiple kernel learning semi-supervised method Infinite ensemble learning

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Robust semi-supervised spatial picture fuzzy clustering with local membership and KL-divergence for image segmentation

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第4期13卷 963-987页

作者： Wu, Chengmao Zhang, Jiajia Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Peoples R China

Aiming at existing symmetric regularized picture fuzzy clustering with weak robustness, and it is difficult to meet the need for image segmentation in the presence of high noise. Hence, a robust dynamic semi-supervised symmetric regularized picture fuzzy clustering with KL-divergence and spatial information constraints is presented in this paper. Firstly, a weighted squared Euclidean distance from current pixel value, its neighborhood mean and median to clustering center is firstly proposed, and it is embedded into the objective function of symmetric regularized picture fuzzy clustering to obtain spatial picture fuzzy clustering. Secondly, the idea of maximum entropy fuzzy clustering is introduced into picture fuzzy clustering, and an entropy-based picture fuzzy clustering with clear physical meaning is constructed to avoid the problem of selecting weighted factors. Subsequently, the prior information of the current pixel is obtained by means of weighted local membership of neighborhood pixels, and it is embedded into the objective function of maximum entropy picture fuzzy clustering with multiple complementary spatial information constraints through KL-divergence, a robust dynamic semi-supervised picture fuzzy clustering optimization model and its iterative algorithm are given. In the end, this proposed algorithm is strictly proved to be convergent by Zangwill theorem. The experiments on various images and standard datasets illustrate how our proposed algorithm works. This proposed algorithm has excellent segmentation performance and anti-noise robustness, and outperforms eight state-of-the-art fuzzy or picture fuzzy clustering-related algorithms in the presence of high noise.

关键词： Image segmentation Picture fuzzy clustering Spatial neighborhood information semi-supervised method KL-divergence Local membership

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