In this paper, we propose a novel mutual consistency network (MC-Net+) to
effectively exploit the unlabeled data for semi-supervised medical image
segmentation. The MC-Net+ model is motivated by the observation that deep
models trained with limited annotations are prone to output highly uncertain
and easily mis-classified predictions in the ambiguous regions (e.g., adhesive
edges or thin branches) for medical image segmentation. Leveraging these
challenging samples can make the semi-supervised segmentation model training
more effective. Therefore, our proposed MC-Net+ model consists of two new
designs. First, the model contains one shared encoder and multiple slightly
different decoders (i.e., using different up-sampling strategies). The
statistical discrepancy of multiple decoders' outputs is computed to denote the
model's uncertainty, which indicates the unlabeled hard regions. Second, we
apply a novel mutual consistency constraint between one decoder's probability
output and other decoders' soft pseudo labels. In this way, we minimize the
discrepancy of multiple outputs (i.e., the model uncertainty) during training
and force the model to generate invariant results in such challenging regions,
aiming at regularizing the model training. We compared the segmentation results
of our MC-Net+ model with five state-of-the-art semi-supervised approaches on
three public medical datasets. Extension experiments with two standard
semi-supervised settings demonstrate the superior performance of our model over
other methods, which sets a new state of the art for semi-supervised medical
image segmentation. Our code is released publicly at
this https URL

本文提出了一种新颖的互相关网络（MC-Net+），通过有效地利用未标记数据进行半监督医学图像分割。我们在三个公共医学数据集上比较了我们 MC-Net+ 模型的分割结果与五种半监督方法的结果，实验表明我们的模型在半监督下分割效果最优，树立了半监督医学图像分割的新标准。

半监督医学图像分割的互一致性学习

Mutual Consistency Learning for Semi-supervised Medical Image  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。