Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images

• URL: http://arxiv.org/abs/2110.01761v1
• Date: Tue, 5 Oct 2021 00:40:43 GMT
• Title: Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images
• Authors: Kang Zhou, Jing Li, Weixin Luo, Zhengxin Li, Jianlong Yang, Huazhu Fu, Jun Cheng, Jiang Liu and Shenghua Gao
• Abstract summary: Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. We propose a novel Proxy-bridged Image Reconstruction Network ( ProxyAno) for anomaly detection in medical images.
• Score: 59.700111685673846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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