Recently, several Bayesian deep learning methods have been proposed for
semi-supervised medical image segmentation. Although they have achieved
promising results on medical benchmarks, some problems are still existing.
Firstly, their overall architectures belong to the discriminative models, and
hence, in the early stage of training, they only use labeled data for training,
which might make them overfit to the labeled data. Secondly, in fact, they are
only partially based on Bayesian deep learning, as their overall architectures
are not designed under the Bayesian framework. However, unifying the overall
architecture under the Bayesian perspective can make the architecture have a
rigorous theoretical basis, so that each part of the architecture can have a
clear probabilistic interpretation. Therefore, to solve the problems, we
propose a new generative Bayesian deep learning (GBDL) architecture. GBDL
belongs to the generative models, whose target is to estimate the joint
distribution of input medical volumes and their corresponding labels.
Estimating the joint distribution implicitly involves the distribution of data,
so both labeled and unlabeled data can be utilized in the early stage of
training, which alleviates the potential overfitting problem. Besides, GBDL is
completely designed under the Bayesian framework, and thus we give its full
Bayesian formulation, which lays a theoretical probabilistic foundation for our
architecture. Extensive experiments show that our GBDL outperforms previous
state-of-the-art methods in terms of four commonly used evaluation indicators
on three public medical datasets.

本文提出一种新的生成贝叶斯深度学习（GBDL）体系结构，可用于半监督医学图像分割任务，结构设计完全基于贝叶斯理论，同时融合了生成模型和生成贝叶斯深度学习的特点，可以利用标记和未标记的数据对训练进行优化，实验证明，该方法对于三个公共的医学数据集，在四个常用的评估指标上优于之前的最新技术。