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Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix

文献类型：期刊论文

作者Qiao, Pengchong5,7; Li, Han2,3; Song, Guoli5; Han, Hu3,4,5; Gao, Zhiqiang5; Tian, Yonghong5,7; Liang, Yongsheng1,5; Li, Xi6; Zhou, S. Kevin2,3; Chen, Jie5,7
刊名IEEE TRANSACTIONS ON MEDICAL IMAGING
出版日期2023-05-01
卷号42期号:5页码:1546-1562
ISSN号0278-0062
关键词Lesions Image segmentation Computed tomography Uncertainty Training Predictive models Data models Semi-supervised learning lesion segmentation unreliable pseudo labels
DOI10.1109/TMI.2022.3232572
英文摘要Semi-supervised learning (SSL) methods show their powerful performance to deal with the issue of data shortage in the field of medical image segmentation. However, existing SSL methods still suffer from the problem of unreliable predictions on unannotated data due to the lack of manual annotations for them. In this paper, we propose an unreliability-diluted consistency training (UDiCT) mechanism to dilute the unreliability in SSL by assembling reliable annotated data into unreliable unannotated data. Specifically, we first propose an uncertainty-based data pairing module to pair annotated data with unannotated data based on a complementary uncertainty pairing rule, which avoids two hard samples being paired off. Secondly, we develop SwapMix, a mixed sample data augmentation method, to integrate annotated data into unannotated data for training our model in a low-unreliability manner. Finally, UDiCT is trained by minimizing a supervised loss and an unreliability-diluted consistency loss, which makes our model robust to diverse backgrounds. Extensive experiments on three chest CT datasets show the effectiveness of our method for semi-supervised CT lesion segmentation.
资助项目Natural Science Foundation of China[62176249] ; Natural Science Foundation of China[32071459] ; Natural Science Foundation of China[61972217] ; Natural Science Foundation of China[62006133] ; Natural Science Foundation of China[62271465] ; Natural Science Foundation of China[62081360152] ; Natural Science Foundation of Guangdong Province in China[2019B1515120049] ; Natural Science Foundation of Guangdong Province in China[2020B1111340056]
WOS研究方向Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000982483400026
源URL[http://119.78.100.204/handle/2XEOYT63/21471]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhou, S. Kevin; Chen, Jie
作者单位1.Harbin Inst Technol, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China
2.Univ Sci & Technol China, Suzhou Inst Adv Res, Ctr Med Imaging Robot Analyt Comp & Learning MIRAC, Sch Biomed Engn, Hefei 230052, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100045, Peoples R China
4.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
5.Peng Cheng Lab, Shenzhen 518055, Peoples R China
6.Peking Univ, Dept Gastroenterol, Shenzhen Hosp, Shenzhen 518036, Peoples R China
7.Peking Univ, Sch Elect & Comp Engn, Shenzhen 518055, Peoples R China
推荐引用方式
GB/T 7714		 Qiao, Pengchong,Li, Han,Song, Guoli,et al. Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2023,42(5):1546-1562.
APA Qiao, Pengchong.,Li, Han.,Song, Guoli.,Han, Hu.,Gao, Zhiqiang.,...&Chen, Jie.(2023).Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix.IEEE TRANSACTIONS ON MEDICAL IMAGING,42(5),1546-1562.
MLA Qiao, Pengchong,et al."Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix".IEEE TRANSACTIONS ON MEDICAL IMAGING 42.5(2023):1546-1562.

入库方式： OAI收割

来源：计算技术研究所

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