Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation

A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, which is often expensive and time consuming to collect. This article presents a new semisupervised method for medical image segmentation, where the network is optimized by a weighted combination of a common supervised loss only for the labeled inputs and a regularization loss for both the labeled and unlabeled data. To utilize the unlabeled data, our method encourages consistent predictions of the network-in-training for the same input under different perturbations. With the semisupervised segmentation tasks, we introduce a transformation-consistent strategy in the self-ensembling model to enhance the regularization…

• NN
  National Natural Science Foundation of China

  Award: U1813204
• IA
  Innovation and Technology Fund

  Awards: ITS/426/17FP, ITS/311/18FP