分割
计算机科学
人工智能
过度拟合
数字化病理学
模式识别(心理学)
点(几何)
聚类分析
像素
基本事实
代表(政治)
计算机视觉
机器学习
人工神经网络
数学
政治
政治学
法学
几何学
作者
Yi Lin,Zhaowei Qu,Hao Chen,Zhongke Gao,Yuexiang Li,Lili Xia,Kai Ma,Yefeng Zheng,Kwang‐Ting Cheng
出处
期刊:Cornell University - arXiv
日期:2022-01-01
被引量:1
标识
DOI:10.48550/arxiv.2202.08195
摘要
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. Codes are available at https://github.com/hust-linyi/SC-Net.
科研通智能强力驱动
Strongly Powered by AbleSci AI