ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation

Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation…

• NN
  National Natural Science Foundation of China

  Awards: 62250710165, 62131015, U23A20295
• SA
  Science and Technology Commission of Shanghai Municipality

  Award: 21010502600