All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation
文献类型：期刊论文
| 作者 | Xu, Zhe3; Wang, Yixin1; Lu, Donghuan4,5; Yu, Lequan2; Yan, Jiangpeng7; Luo, Jie6; Ma, Kai4,5; Zheng, Yefeng4,5; Tong, Raymond Kai-yu3 |
| 刊名 | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
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| 出版日期 | 2022-07-01 |
| 卷号 | 26期号:7页码:3174-3184 |
| 关键词 | Image segmentation Prototypes Biomedical imaging Perturbation methods Reliability Feature extraction Training Medical image segmentation prototype learning semi-supervised learning |
| ISSN号 | 2168-2194 |
| DOI | 10.1109/JBHI.2022.3162043 |
| 英文摘要 | Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially, the consistency-based approaches have attracted more attention for their superior performance, wherein the real labels are only utilized to supervise their paired images via supervised loss while the unlabeled images are exploited by enforcing the perturbation-based "unsupervised" consistency without explicit guidance from those real labels. However, intuitively, the expert-examined real labels contain more reliable supervision signals. Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training? To this end, we discard the previous perturbation-based consistency but absorb the essence of non-parametric prototype learning. Based on the prototypical networks, we then propose a novel cyclic prototype consistency learning (CPCL) framework, which is constructed by a labeled-to-unlabeled (L2U) prototypical forward process and an unlabeled-to-labeled (U2L) backward process. Such two processes synergistically enhance the segmentation network by encouraging morediscriminative and compact features. In this way, our framework turns previous "unsupervised" consistency into new "supervised" consistency, obtaining the "all-around real label supervision" property of our method. Extensive experiments on brain tumor segmentation from MRI and kidney segmentation from CT images show that our CPCL can effectively exploit the unlabeled data and outperform other state-of-the-art semi-supervised medical image segmentation methods. |
| 资助项目 | National Key R&D Program of China[2018YFC2000702] ; General Research Fund from Research Grant Council of Hong Kong[14205419] ; Tencent Healthcare (Shenzhen) Co., LTD ; Tencent Jarvis Lab |
| WOS研究方向 | Computer Science ; Mathematical & Computational Biology ; Medical Informatics |
| 语种 | 英语 |
| WOS记录号 | WOS:000819832600033 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19510]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Lu, Donghuan; Tong, Raymond Kai-yu |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Hong Kong, Dept Stat & Actuarial Sci, Hong Kong, Peoples R China 3.Chinese Univ Hong Kong, Dept Biomed Engn, Hong Kong 999077, Peoples R China 4.Tencent Hlthcare Shenzhen Co LTD, Shenzhen 518000, Peoples R China 5.Tencent Jarvis Lab, Shenzhen 518000, Peoples R China 6.Harvard Med Sch, Brigham & Womens Hosp, Boston, MA 02115 USA 7.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Zhe,Wang, Yixin,Lu, Donghuan,et al. All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2022,26(7):3174-3184. |
| APA | Xu, Zhe.,Wang, Yixin.,Lu, Donghuan.,Yu, Lequan.,Yan, Jiangpeng.,...&Tong, Raymond Kai-yu.(2022).All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,26(7),3174-3184. |
| MLA | Xu, Zhe,et al."All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 26.7(2022):3174-3184. |
入库方式： OAI收割
来源：计算技术研究所
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