Knowledge Commons of Institute of Automation,CAS
Semi-supervised Lesion Detection with Reliable Label Propagation and Missing Label Mining | |
Wang, Zhuo1,2![]() ![]() ![]() | |
2019-10 | |
会议名称 | Chinese Conference on Pattern Recognition and Computer Vision (PRCV) |
会议日期 | 2019-11 |
会议地点 | 中国西安 |
摘要 | Annotations for medical images are very hard to acquire as it requires specific domain knowledge. Therefore, performance of deep learning algorithms on medical image processing is largely hindered by the scarcity of large-scale labeled data. To address this challenge, we propose a semi-supervised learning method for lesion detection from CT images which exploits a key characteristic of the volumetric medical data, i.e. adjacent slices in the axial axis resemble each other, or say they bear some kind of continuity. Specifically, by exploiting such a prior, a semi-supervised scheme is adopted to propagate bounding box annotations to adjacent CT slices to obtain more training data with fewer false positives and more true positives. Furthermore, considering that the NIH DeepLesion dataset has many missing labels, we develop a missing ground truth mining process by considering the continuity (or appearance-consistency) of multi-slice axial CT images. Experimental results on the NIH DeepLesion dataset demonstrate the effectiveness our methods for both semi-supervised label propagation and missing label mining. |
收录类别 | EI |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39150 |
专题 | 复杂系统认知与决策实验室_智能系统与工程 |
作者单位 | 1.CRISE, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Deepwise AI Lab |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wang, Zhuo,Li, Zihao,Zhang, Shu,et al. Semi-supervised Lesion Detection with Reliable Label Propagation and Missing Label Mining[C],2019. |
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