Corpus ID: 237605544

Mixed-supervised segmentation: Confidence maximization helps knowledge distillation

  title={Mixed-supervised segmentation: Confidence maximization helps knowledge distillation},
  author={Bingyuan Liu and Christian Desrosiers and Ismail Ben Ayed and Jos{\'e} Dolz},
Despite achieving promising results in a breadth of medical image segmentation tasks, deep neural networks (DNNs) require large training datasets with pixel-wise annotations. Obtaining these curated datasets is a cumbersome process which limits the applicability of DNNs in scenarios where annotated images are scarce. Mixed supervision is an appealing alternative for mitigating this obstacle. In this setting, only a small fraction of the data contains complete pixel-wise annotations and other… Expand


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ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation
  • Xinyue Huo, Lingxi Xie, +4 authors Qi Tian
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2021
The Asynchronous Teacher-Student Optimization (ATSO) algorithm is proposed, which breaks up continual learning from teacher to student and partitions the unlabeled training data into two subsets and alternately uses one subset to fine-tune the model which updates the labels on the other. Expand