Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training

  • Zahil Shanis
  • Samuel Gerber
  • Mingchen Gao
  • Andinet EnquobahrieEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)


Advances in deep learning techniques have led to compelling achievements in medical image analysis. However, performance of neural network models degrades drastically if the test data is from a domain different from training data. In this paper, we present and evaluate a novel unsupervised domain adaptation (DA) framework for semantic segmentation which uses self ensembling and adversarial training methods to effectively tackle domain shift between MR images. We evaluate our method on two publicly available MRI dataset to address two different types of domain shifts: On the BraTS dataset [11] to mitigate domain shift between high grade and low grade gliomas and on the SCGM dataset [13] to tackle cross institutional domain shift. Through extensive evaluation, we show that our method achieves favorable results on both datasets.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zahil Shanis
    • 1
    • 2
  • Samuel Gerber
    • 1
  • Mingchen Gao
    • 2
  • Andinet Enquobahrie
    • 1
    Email author
  1. 1.Kitware Inc.CarrboroUSA
  2. 2.SUNY at BuffaloBuffaloUSA

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