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.

本文提出了一种简单而高效的竞争集成教师学生框架，用于从 3D MR 图像中半监督地分割左心房，通过利用无标记数据并提供可靠信息给教师模型，以实现不同学生模型之间的协作学习并达到更好的性能。通过在公开 Left Atrium（LA）数据集上评估，我们的方法在有效利用无标记数据方面取得了显著的性能提升，并优于几种现有的半监督方法。

基于竞争集成师生框架的半监督左心房 MRI 分割

Competitive Ensembling Teacher-Student Framework for Semi-Supervised  Left Atrium MRI Segmentation

Semi-supervised learning has attracted great attention in the field of
machine learning, especially for medical image segmentation tasks, since it
alleviates the heavy burden of collecting abundant densely annotated data for
training. However, most of existing methods underestimate the importance of
challenging regions (e.g. small branches or blurred edges) during training. We
believe that these unlabeled regions may contain more crucial information to
minimize the uncertainty prediction for the model and should be emphasized in
the training process. Therefore, in this paper, we propose a novel Mutual
Consistency Network (MC-Net) for semi-supervised left atrium segmentation from
3D MR images. Particularly, our MC-Net consists of one encoder and two slightly
different decoders, and the prediction discrepancies of two decoders are
transformed as an unsupervised loss by our designed cycled pseudo label scheme
to encourage mutual consistency. Such mutual consistency encourages the two
decoders to have consistent and low-entropy predictions and enables the model
to gradually capture generalized features from these unlabeled challenging
regions. We evaluate our MC-Net on the public Left Atrium (LA) database and it
obtains impressive performance gains by exploiting the unlabeled data
effectively. Our MC-Net outperforms six recent semi-supervised methods for left
atrium segmentation, and sets the new state-of-the-art performance on the LA
database.

提出了一种基于 MC-Net 的半监督学习算法，通过设计的循环伪标签方案，利用未标记数据强化了训练过程，从而提高了心房图像分割性能。与其他半监督方法相比，该算法在公共数据集上表现出更好的性能。

半监督互相一致训练下的左心房分割

Semi-supervised Left Atrium Segmentation with Mutual Consistency  Training

Training deep convolutional neural networks usually requires a large amount
of labeled data. However, it is expensive and time-consuming to annotate data
for medical image segmentation tasks. In this paper, we present a novel
uncertainty-aware semi-supervised framework for left atrium segmentation from
3D MR images. Our framework can effectively leverage the unlabeled data by
encouraging consistent predictions of the same input under different
perturbations. Concretely, the framework consists of a student model and a
teacher model, and the student model learns from the teacher model by
minimizing a segmentation loss and a consistency loss with respect to the
targets of the teacher model. We design a novel uncertainty-aware scheme to
enable the student model to gradually learn from the meaningful and reliable
targets by exploiting the uncertainty information. Experiments show that our
method achieves high performance gains by incorporating the unlabeled data. Our
method outperforms the state-of-the-art semi-supervised methods, demonstrating
the potential of our framework for the challenging semi-supervised problems.

本文介绍了一种新颖的基于不确定性的半监督深度学习框架，可将未标记的数据有效地整合到左心房分割任务中，并在医学图像分析领域中表现出潜在的半监督解决方案。