Open-Set OCT Image Recognition with Synthetic Learning

Yuting Xiao, Shenghua Gao, Zhengjie Chai, Kang Zhou, Tianyang Zhang, Yitian Zhao, Jun Cheng, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


Due to new eye diseases discovered every year, doctors may encounter some rare or unknown diseases. Similarly, in medical image recognition field, many practical medical classification tasks may encounter the case where some testing samples belong to some rare or unknown classes that have never been observed or included in the training set, which is termed as an open-set problem. As rare diseases samples are difficult to be obtained and included in the training set, it is reasonable to design an algorithm that recognizes both known and unknown diseases. Towards this end, this paper leverages a novel generative adversarial network (GAN) based synthetic learning for open-set retinal optical coherence tomography (OCT) image recognition. Specifically, we first train an auto-encoder GAN and a classifier to reconstruct and classify the observed images, respectively. Then a subspace-constrained synthesis loss is introduced to generate images that locate near the boundaries of the subspace of images corresponding to each observed disease, meanwhile, these images cannot be classified by the pre-trained classifier. In other words, these synthesized images are categorized into an unknown class. In this way, we can generate images belonging to the unknown class, and add them into the original dataset to retrain the classifier for the unknown disease discovery.

Original languageEnglish
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538693308
Publication statusPublished - Apr 2020
Externally publishedYes
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: 3 Apr 20207 Apr 2020

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Country/TerritoryUnited States
CityIowa City


  • Generative Adversarial Network
  • Open-set
  • Subspace-constrained Synthesis Loss

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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