Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images

@inproceedings{Zhang2017DeepAN,
  title={Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images},
  author={Yizhe Zhang and Lin Yang and Jianxu Chen and Maridel Fredericksen and David P. Hughes and Danny Ziyi Chen},
  booktitle={MICCAI},
  year={2017}
}
Semantic segmentation is a fundamental problem in biomedical image analysis. In biomedical practice, it is often the case that only limited annotated data are available for model training. Unannotated images, on the other hand, are easier to acquire. How to utilize unannotated images for training effective segmentation models is an important issue. In this paper, we propose a new deep adversarial network (DAN) model for biomedical image segmentation, aiming to attain consistently good… CONTINUE READING
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