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Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12365)

Abstract

Anomaly detection in retinal image refers to the identification of abnormality caused by various retinal diseases/lesions, by only leveraging normal images in training phase. Normal images from healthy subjects often have regular structures (e.g., the structured blood vessels in the fundus image, or structured anatomy in optical coherence tomography image). On the contrary, the diseases and lesions often destroy these structures. Motivated by this, we propose to leverage the relation between the image texture and structure to design a deep neural network for anomaly detection. Specifically, we first extract the structure of the retinal images, then we combine both the structure features and the last layer features extracted from original health image to reconstruct the original input healthy image. The image feature provides the texture information and guarantees the uniqueness of the image recovered from the structure. In the end, we further utilize the reconstructed image to extract the structure and measure the difference between structure extracted from original and the reconstructed image. On the one hand, minimizing the reconstruction difference behaves like a regularizer to guarantee that the image is corrected reconstructed. On the other hand, such structure difference can also be used as a metric for normality measurement. The whole network is termed as P-Net because it has a “P” shape. Extensive experiments on RESC dataset and iSee dataset validate the effectiveness of our approach for anomaly detection in retinal images. Further, our method also generalizes well to novel class discovery in retinal images and anomaly detection in real-world images.

Keywords

Structure-texture relation Anomaly detection Novel class discovery 

Notes

Acknowledge

The work was supported by National Key R&D Program of China (2018AAA0100704), NSFC #61932020, Guangdong Provincial Key Laboratory (2020B121201001), ShanghaiTech-Megavii Joint Lab, and ShanghaiTech-UnitedImaging Joint Lab.

Supplementary material

504476_1_En_22_MOESM1_ESM.pdf (727 kb)
Supplementary material 1 (pdf 726 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina
  2. 2.Cixi Institute of Biomedical Engineering, Chinese Academy of SciencesBeijingChina
  3. 3.UBTech ResearchShenzhenChina
  4. 4.Southern University of Science and TechnologyShenzhenChina
  5. 5.Shanghai Engineering Research Center of Intelligent Vision and ImagingShanghaiChina

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