CASIA OpenIR  > 复杂系统认知与决策实验室  > 智能系统与工程
Semi-supervised Lesion Detection with Reliable Label Propagation and Missing Label Mining
Wang, Zhuo1,2; Li, Zihao1,2; Zhang, Shu3; Zhang, Junge1,2; Huang, Kaiqi1,2
2019-10
Conference NameChinese Conference on Pattern Recognition and Computer Vision (PRCV)
Conference Date2019-11
Conference Place中国西安
Abstract

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.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39150
Collection复杂系统认知与决策实验室_智能系统与工程
Affiliation1.CRISE, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Deepwise AI Lab
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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