Human-Machine Collaboration for Medical Image Segmentation

  title={Human-Machine Collaboration for Medical Image Segmentation},
  author={F. Last and Tassilo Klein and Mahdyar Ravanbakhsh and Moin Nabi and K. Batmanghelich and Volker Tresp},
  journal={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  • F. Last, T. Klein, Volker Tresp
  • Published 1 May 2020
  • Computer Science
  • ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Image segmentation is a ubiquitous step in almost any medical image study. Deep learning-based approaches achieve state-of-the-art in the majority of image segmentation benchmarks. However, end-to-end training of such models requires sufficient annotation. In this paper, we propose a method based on conditional Generative Adversarial Network (cGAN) to address segmentation in semi-supervised setup and in a human-in-the-loop fashion. More specifically, we use the generator in the GAN to… 

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