Corpus ID: 221397174

Quality-aware semi-supervised learning for CMR segmentation

@article{Ruijsink2020QualityawareSL,
  title={Quality-aware semi-supervised learning for CMR segmentation},
  author={B. Ruijsink and Esther Puyol-Ant{\'o}n and Ye Li and Wenja Bai and E. Kerfoot and R. Razavi and A. P. King},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.00584}
}
  • B. Ruijsink, Esther Puyol-Antón, +4 authors A. P. King
  • Published 2020
  • Computer Science, Engineering
  • ArXiv
  • One of the challenges in developing deep learning algorithms for medical image segmentation is the scarcity of annotated training data. To overcome this limitation, data augmentation and semi-supervised learning (SSL) methods have been developed. However, these methods have limited effectiveness as they either exploit the existing data set only (data augmentation) or risk negative impact by adding poor training examples (SSL). Segmentations are rarely the final product of medical image analysis… CONTINUE READING

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