Efficient Active Learning for Image Classification and Segmentation Using a Sample Selection and Conditional Generative Adversarial Network

  • Dwarikanath MahapatraEmail author
  • Behzad Bozorgtabar
  • Jean-Philippe Thiran
  • Mauricio Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about \(35\%\) of the full dataset, thus saving significant time and effort over conventional methods.



The authors acknowledge the support from SNSF project grant number 169607.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Dwarikanath Mahapatra
    • 1
    Email author
  • Behzad Bozorgtabar
    • 2
  • Jean-Philippe Thiran
    • 2
  • Mauricio Reyes
    • 3
  1. 1.IBM Research AustraliaMelbourneAustralia
  2. 2.Ecole Polytechnique Federale de LausanneLausanneSwitzerland
  3. 3.University of BernBernSwitzerland

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