ST-LP: self-training and label propagation for semi-supervised classification
Due to the particularly high costs of manual data labeling, especially in the field of medical imaging, and the necessity for specialized knowledge, there is a growing interest in semi-supervised methods. In this paper, a novel framework of Self-Training with Label Propagation (ST-LP) is proposed fo...
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| Published in: | Multimedia tools and applications Vol. 83; no. 41; pp. 89335 - 89353 |
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| Format: | Journal Article |
| Language: | English |
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01.12.2024
Springer Nature B.V |
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| ISSN: | 1573-7721, 1380-7501, 1573-7721 |
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| Abstract | Due to the particularly high costs of manual data labeling, especially in the field of medical imaging, and the necessity for specialized knowledge, there is a growing interest in semi-supervised methods. In this paper, a novel framework of Self-Training with Label Propagation (ST-LP) is proposed for semi-supervised classification. It integrates self-training and label propagation to address the challenge of limited labeled data in classification tasks, a concern exacerbated by the especially expensive nature of data labeling in the medical domain. Our method involves leveraging two soft pseudo-labels generated from a pre-training fine-tuned model and label propagation scheme as inputs for a pseudo-label prediction module. Subsequently, confident predictions from this model are selected as pseudo-labeled data. The effectiveness of our approach is demonstrated through experiments conducted on diverse datasets, including the MNIST dataset and two medical classification datasets: ISIC2018 and MURA. Experimental results demonstrate that our method consistently achieved comparable or outstanding results when dealing with large amounts of unlabeled data. |
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| AbstractList | Due to the particularly high costs of manual data labeling, especially in the field of medical imaging, and the necessity for specialized knowledge, there is a growing interest in semi-supervised methods. In this paper, a novel framework of Self-Training with Label Propagation (ST-LP) is proposed for semi-supervised classification. It integrates self-training and label propagation to address the challenge of limited labeled data in classification tasks, a concern exacerbated by the especially expensive nature of data labeling in the medical domain. Our method involves leveraging two soft pseudo-labels generated from a pre-training fine-tuned model and label propagation scheme as inputs for a pseudo-label prediction module. Subsequently, confident predictions from this model are selected as pseudo-labeled data. The effectiveness of our approach is demonstrated through experiments conducted on diverse datasets, including the MNIST dataset and two medical classification datasets: ISIC2018 and MURA. Experimental results demonstrate that our method consistently achieved comparable or outstanding results when dealing with large amounts of unlabeled data. |
| Author | Wang, Yu-An Li, Hao-Ting Lin, Chih-Wen Lin, Tzu-Chieh Yang, Yue-Lin Chiang, Chen-Kuo |
| Author_xml | – sequence: 1 givenname: Chih-Wen surname: Lin fullname: Lin, Chih-Wen organization: Department of Medical Imaging, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Department of School of Medicine, Tzu Chi University – sequence: 2 givenname: Chen-Kuo orcidid: 0000-0001-5276-1109 surname: Chiang fullname: Chiang, Chen-Kuo email: ckchiang@cs.ccu.edu.tw organization: Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-tech Innovations and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University – sequence: 3 givenname: Yu-An surname: Wang fullname: Wang, Yu-An organization: Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-tech Innovations and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University – sequence: 4 givenname: Yue-Lin surname: Yang fullname: Yang, Yue-Lin organization: Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-tech Innovations and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University – sequence: 5 givenname: Hao-Ting surname: Li fullname: Li, Hao-Ting organization: Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-tech Innovations and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University – sequence: 6 givenname: Tzu-Chieh surname: Lin fullname: Lin, Tzu-Chieh organization: Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-tech Innovations and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University |
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| CitedBy_id | crossref_primary_10_1016_j_jmsy_2025_06_011 |
| Cites_doi | 10.1109/ICCV48922.2021.00873 10.1109/ICCC47050.2019.9064268 10.18653/v1/D19-1514 10.1016/j.ins.2018.12.057 10.1007/978-3-319-46493-0_39 10.1109/TMI.2020.2995518 10.1109/CVPR46437.2021.01071 10.3389/fcomp.2023.1114186 10.1109/CVPR.2016.90 10.1109/CVPR42600.2020.01070 10.1109/CVPR.2019.00521 10.1007/978-3-030-58555-6_15 10.1109/ISBI.2018.8363547 10.1109/TMI.2019.2903434 10.1007/978-3-319-66185-8_29 10.1109/CVPR.2018.00353 10.1109/CVPR46437.2021.00685 10.23919/ChiCC.2019.8866590 10.1109/CVPR42600.2020.01059 10.1109/MSP.2012.2211477 10.1007/978-3-030-58601-0_32 10.1109/CVPR.2017.243 |
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| Title | ST-LP: self-training and label propagation for semi-supervised classification |
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