Truly Generalizable Radiograph Segmentation With Conditional Domain Adaptation

@article{Oliveira2020TrulyGR,
  title={Truly Generalizable Radiograph Segmentation With Conditional Domain Adaptation},
  author={Hugo Neves de Oliveira and Edemir Ferreira and Jefersson Alex dos Santos},
  journal={IEEE Access},
  year={2020},
  volume={8},
  pages={84037-84062}
}
Digitization techniques for biomedical images yield disparate visual patterns in radiological exams. These pattern differences, which can be viewed as a domain-shift problem, may hamper the use of data-driven approaches for inference over these images, such as Deep Neural Networks. Another noticeable difficulty in this field is the lack of labeled data, even though in many cases there is an abundance of unlabeled data available. Therefore, an important step in improving the generalization… Expand
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