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Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images | |
2022-03-01 | |
发表期刊 | IEEE TRANSACTIONS ON MEDICAL IMAGING |
ISSN | 0278-0062 |
EISSN | 1558-254X |
卷号 | 41期号:3 |
发表状态 | 已发表 |
DOI | 10.1109/TMI.2021.3118223 |
摘要 | Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a self-reconstruction framework, which tends to learn an identity mapping and reduces the sensitivity to anomalies. To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images. Specifically, we use an intermediate proxy to bridge the input image and the reconstructed image. We study different proxy types, and we find that the superpixel-image (SI) is the best one. We set all pixels' intensities within each superpixel as their average intensity, and denote this image as SI. The proposed ProxyAno consists of two modules, a Proxy Extraction Module and an Image Reconstruction Module. In the Proxy Extraction Module, a memory is introduced to memorize the feature correspondence for normal image to its corresponding SI, while the memorized correspondence does not apply to the abnormal images, which leads to the information loss for abnormal image and facilitates the anomaly detection. In the Image Reconstruction Module, we map an SI to its reconstructed image. Further, we crop a patch from the image and paste it on the normal SI to mimic the anomalies, and enforce the network to reconstruct the normal image even with the pseudo abnormal SI. In this way, our network enlarges the reconstruction error for anomalies. Extensive experiments on brain MR images, retinal OCT images and retinal fundus images verify the effectiveness of our method for both image-level and pixel-level anomaly detection. IEEE |
关键词 | Extraction Image reconstruction Magnetic resonance imaging Medical imaging Ophthalmology Superpixels Anomaly detection Images reconstruction Proxy Pseudo anomaly Reconstructed image Reconstruction networks Self reconstruction Super pixels Superpixel-image Training sets |
URL | 查看原文 |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000766268800008 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
EI入藏号 | 20214311047840 |
EI主题词 | Anomaly detection |
EI分类号 | 461.1 Biomedical Engineering ; 461.6 Medicine and Pharmacology ; 701.2 Magnetism: Basic Concepts and Phenomena ; 746 Imaging Techniques ; 802.3 Chemical Operations |
原始文献类型 | Article in Press |
来源库 | IEEE |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/135700 |
专题 | 生物医学工程学院 信息科学与技术学院 信息科学与技术学院_PI研究组_高盛华组 信息科学与技术学院_博士生 |
作者单位 | 1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China 3.Agency for Science Technology and Research (A*STAR), Institute of High Performance Computing (IHPC), Singapore 4.Agency for Science Technology and Research (A*STAR), Institute for Infocomm Research, Singapore 5.Department of Computer Science and Engineering, Southern University of Science and Technology, Guangdong, Shenzhen, China |
第一作者单位 | 信息科学与技术学院 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Kang Zhou,Jing Li,Weixin Luo,et al. Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2022,41(3). |
APA | Kang Zhou.,Jing Li.,Weixin Luo.,Zhengxin Li.,Jianlong Yang.,...&Shenghua Gao.(2022).Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images.IEEE TRANSACTIONS ON MEDICAL IMAGING,41(3). |
MLA | Kang Zhou,et al."Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images".IEEE TRANSACTIONS ON MEDICAL IMAGING 41.3(2022). |
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