CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation

  • Peng Liu
  • Bin Kong
  • Zhongyu Li
  • Shaoting Zhang
  • Ruogu FangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)


Recently, deep neural networks have demonstrated comparable and even better performance with board-certified ophthalmologists in well-annotated datasets. However, the diversity of retinal imaging devices poses a significant challenge: domain shift, which leads to performance degradation when applying the deep learning models to new testing domains. In this paper, we propose a novel unsupervised domain adaptation framework, called Collaborative Feature Ensembling Adaptation (CFEA), to effectively overcome this challenge. Our proposed CFEA is an interactive paradigm which presents an exquisite of collaborative adaptation through both adversarial learning and ensembling weights. In particular, we simultaneously achieve domain-invariance and maintain an exponential moving average of the historical predictions, which achieves a better prediction for the unlabeled data, via ensembling weights during training. Without annotating any sample from the target domain, multiple adversarial losses in encoder and decoder layers guide the extraction of domain-invariant features to confuse the domain classifier and meanwhile benefit the ensembling of smoothing weights. Comprehensive experimental results demonstrate that our CFEA model can overcome performance degradation and outperform the state-of-the-art methods in segmenting retinal optic disc and cup from fundus images.


Domain adaptation Adversarial learning Ensembling Segmentation Retinal fundus images 



Research reported in this publication is partially supported by the National Science Foundation under Grant No. IIS-1564892 and IIS-1908299, the University of Florida Informatics Institute Junior SEED Program (00129436), the University of Florida Informatics Institute SEED Funds, and the UF Clinical and Translational Science Institute, which is supported in part by the NIH National Center for Advancing Translational Sciences under award number UL1 TR001427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health and the National Science Foundation.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peng Liu
    • 1
  • Bin Kong
    • 2
  • Zhongyu Li
    • 3
  • Shaoting Zhang
    • 4
  • Ruogu Fang
    • 1
    Email author
  1. 1.J. Crayton Pruitt Family Department of Biomedical EngineeringUniversity of FloridaGainesvilleUSA
  2. 2.Department of Computer ScienceUNC CharlotteCharlotteUSA
  3. 3.School of Software EngineeringXi’an Jiaotong UniversityXi’anChina
  4. 4.Sensetime ResearchShanghaiChina

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