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@ARTICLE{Ross:134894,
      author       = {T. Ross$^*$ and D. Zimmerer$^*$ and A. Vemuri$^*$ and F.
                      Isensee$^*$ and M. Wiesenfarth$^*$ and S. Bodenstedt and F.
                      Both and P. Kessler and M. Wagner and B. Müller and H.
                      Kenngott and S. Speidel and A. Kopp-Schneider$^*$ and K.
                      Maier-Hein$^*$ and L. Maier-Hein$^*$},
      title        = {{E}xploiting the potential of unlabeled endoscopic video
                      data with self-supervised learning.},
      journal      = {International journal of computer assisted radiology and
                      surgery},
      volume       = {13},
      number       = {6},
      issn         = {1861-6429},
      address      = {Berlin},
      publisher    = {Springer},
      reportid     = {DKFZ-2018-00682},
      pages        = {925 - 933},
      year         = {2018},
      note         = {E132 entspricht E230},
      abstract     = {Surgical data science is a new research field that aims to
                      observe all aspects of the patient treatment process in
                      order to provide the right assistance at the right time. Due
                      to the breakthrough successes of deep learning-based
                      solutions for automatic image annotation, the availability
                      of reference annotations for algorithm training is becoming
                      a major bottleneck in the field. The purpose of this paper
                      was to investigate the concept of self-supervised learning
                      to address this issue.Our approach is guided by the
                      hypothesis that unlabeled video data can be used to learn a
                      representation of the target domain that boosts the
                      performance of state-of-the-art machine learning algorithms
                      when used for pre-training. Core of the method is an
                      auxiliary task based on raw endoscopic video data of the
                      target domain that is used to initialize the convolutional
                      neural network (CNN) for the target task. In this paper, we
                      propose the re-colorization of medical images with a
                      conditional generative adversarial network (cGAN)-based
                      architecture as auxiliary task. A variant of the method
                      involves a second pre-training step based on labeled data
                      for the target task from a related domain. We validate both
                      variants using medical instrument segmentation as target
                      task.The proposed approach can be used to radically reduce
                      the manual annotation effort involved in training CNNs.
                      Compared to the baseline approach of generating annotated
                      data from scratch, our method decreases exploratively the
                      number of labeled images by up to $75\%$ without sacrificing
                      performance. Our method also outperforms alternative methods
                      for CNN pre-training, such as pre-training on publicly
                      available non-medical (COCO) or medical data (MICCAI
                      EndoVis2017 challenge) using the target task (in this
                      instance: segmentation).As it makes efficient use of
                      available (non-)public and (un-)labeled data, the approach
                      has the potential to become a valuable tool for CNN
                      (pre-)training.},
      cin          = {E130 / E132 / E230 / C060},
      ddc          = {610},
      cid          = {I:(DE-He78)E130-20160331 / I:(DE-He78)E132-20160331 /
                      I:(DE-He78)E230-20160331 / I:(DE-He78)C060-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29704196},
      doi          = {10.1007/s11548-018-1772-0},
      url          = {https://inrepo02.dkfz.de/record/134894},
}