Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation.…

• NN
  National Natural Science Foundation of China

  Awards: 92470101, 62171377
• NK
  National Key Research and Development Program of China

  Awards: 2022YFC2009903, 2022YFC2009900