Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo
  Labeling Leveraging Strong and Weak Data Augmentation Strategies
Yifei Chen Chenyan Zhang Yifan Ke Yiyu Huang Xuezhou Dai Feiwei Qin Yongquan Zhang Xiaodong Zhang Changmiao Wang
Traditional supervised learning methods have historically encountered certain
constraints in medical image segmentation due to the challenging collection
process, high labeling cost, low signal-to-noise ratio, and complex features
characterizing biomedical images. This paper proposes a semi-supervised model,
DFCPS, which innovatively incorporates the Fixmatch concept. This significantly
enhances the model's performance and generalizability through data augmentation
processing, employing varied strategies for unlabeled data. Concurrently, the
model design gives appropriate emphasis to the generation, filtration, and
refinement processes of pseudo-labels. The novel concept of
cross-pseudo-supervision is introduced, integrating consistency learning with
self-training. This enables the model to fully leverage pseudo-labels from
multiple perspectives, thereby enhancing training diversity. The DFCPS model is
compared with both baseline and advanced models using the publicly accessible
Kvasir-SEG dataset. Across all four subdivisions containing different
proportions of unlabeled data, our model consistently exhibits superior
performance. Our source code is available at
https://github.com/JustlfC03/DFCPS.