Competitive Ensembling Teacher-Student Framework for Semi-Supervised
  Left Atrium MRI Segmentation
Yuyan Shi Yichi Zhang Shasha Wang
Semi-supervised learning has greatly advanced medical image segmentation
since it effectively alleviates the need of acquiring abundant annotations from
experts and utilizes unlabeled data which is much easier to acquire. Among
existing perturbed consistency learning methods, mean-teacher model serves as a
standard baseline for semi-supervised medical image segmentation. In this
paper, we present a simple yet efficient competitive ensembling teacher student
framework for semi-supervised for left atrium segmentation from 3D MR images,
in which two student models with different task-level disturbances are
introduced to learn mutually, while a competitive ensembling strategy is
performed to ensemble more reliable information to teacher model. Different
from the one-way transfer between teacher and student models, our framework
facilitates the collaborative learning procedure of different student models
with the guidance of teacher model and motivates different training networks
for a competitive learning and ensembling procedure to achieve better
performance. We evaluate our proposed method on the public Left Atrium (LA)
dataset and it obtains impressive performance gains by exploiting the unlabeled
data effectively and outperforms several existing semi-supervised methods.