An Evidential-enhanced Tri-Branch Consistency Learning Method for
  Semi-supervised Medical Image Segmentation
Zhenxi Zhang Heng Zhou Xiaoran Shi Ran Ran Chunna Tian Feng Zhou
Semi-supervised segmentation presents a promising approach for large-scale
medical image analysis, effectively reducing annotation burdens while achieving
comparable performance. This methodology holds substantial potential for
streamlining the segmentation process and enhancing its feasibility within
clinical settings for translational investigations. While cross-supervised
training, based on distinct co-training sub-networks, has become a prevalent
paradigm for this task, addressing critical issues such as predication
disagreement and label-noise suppression requires further attention and
progress in cross-supervised training. In this paper, we introduce an
Evidential Tri-Branch Consistency learning framework (ETC-Net) for
semi-supervised medical image segmentation. ETC-Net employs three branches: an
evidential conservative branch, an evidential progressive branch, and an
evidential fusion branch. The first two branches exhibit complementary
characteristics, allowing them to address prediction diversity and enhance
training stability. We also integrate uncertainty estimation from the
evidential learning into cross-supervised training, mitigating the negative
impact of erroneous supervision signals. Additionally, the evidential fusion
branch capitalizes on the complementary attributes of the first two branches
and leverages an evidence-based Dempster-Shafer fusion strategy, supervised by
more reliable and accurate pseudo-labels of unlabeled data. Extensive
experiments conducted on LA, Pancreas-CT, and ACDC datasets demonstrate that
ETC-Net surpasses other state-of-the-art methods for semi-supervised
segmentation. The code will be made available in the near future at
https://github.com/Medsemiseg.