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2. Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training

Author

Lin, Yi, Qu, Zhiyong, Chen, Hao, Gao, Zhongke, Li, Yuexiang, Xia, Lili, Ma, Kai, Zheng, Yefeng, and Cheng, Kwang-Ting

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. Code: https://github.com/hust-linyi/SC-Net, Comment: Accepted by MedIA

Published
2022
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11. Design and Realization of 108MN Multi-function Testing System

Author

Zheng, Shutao, Yang, Yu, Qu, Zhiyong, Han, Junwei, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Li, Kang, editor, Fei, Minrui, editor, Du, Dajun, editor, Yang, Zhile, editor, and Yang, Dongsheng, editor

Published
2018
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15. Capture Dynamics Modeling and Simulation of the Space Flexible Manipulator

Author

Yu, Simiao, Qu, Zhiyong, Zheng, Shutao, Han, Junwei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Washio, Takashi, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhang, Lin, editor, Song, Xiao, editor, and Wu, Yunjie, editor

Published
2016
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26. Parental Migration and Children's Academic Engagement: The Case of China

Author

Chen, Shuang, Adams, Jennifer, Qu, Zhiyong, Wang, Xiaohua, and Chen, Li

Abstract

In the context of China's increasing rural-urban migration, few studies have investigated how parental migration affects children's experience in school. The high cost of schooling, taken together with the institutional barriers in destination cities, have compelled many rural parents in China to migrate without their children, leaving them in the care of their spouses, grandparents, relatives or other caregivers. Still other parents migrate with their children, many of whom then attend urban migrant schools in their destination city. Understanding the academic engagement of children of migrant workers is particularly salient because the poor qualities of migrant schools, a lack of parental support, and exposure to competing alternatives to schooling may render both migrant children in the cities and left-behind children in the rural villages vulnerable to disengagement, and ultimately school dropout. Using data collected in 2008 in the urban Haidian and Changping districts of Beijing and rural Henan and Shaanxi provinces, the authors of this paper investigate the association between parental migration status and two measures of academic engagement, academic aspirations and the odds of liking school, by comparing migrant children attending migrant schools and left-behind children with their rural counterparts who do not have migrant parents. The authors' findings show that migrant children attending migrant schools have lower academic engagement compared to rural children of non-migrant parents. The correlation between academic engagement and parental migration status can be accounted for in part by the support children receive from family and teachers. The association between certain measures of family and school support and academic engagement also varies by parental migration status: for example, high teacher turnover rates significantly reduce migrant children's odds of liking schools, but do not affect children of non-migrant parents.

Published
2013
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35. Label Propagation for Annotation-Efficient Nuclei Segmentation from Pathology Images

Author

Lin, Yi, Qu, Zhiyong, Chen, Hao, Gao, Zhongke, Li, Yuexiang, Xia, Lili, Ma, Kai, Zheng, Yefeng, and Cheng, Kwang-Ting

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), FOS: Biological sciences, Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM)
Abstract

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. The proposed method achieves label propagation in a coarse-to-fine manner as follows. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H\&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. The source codes for implementing the experiments will be released after acceptance.

Published
2022
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