Deep learning-based semi-supervised learning (SSL) algorithms have led to
promising results in medical images segmentation and can alleviate doctors'
expensive annotations by leveraging unlabeled data. However, most of the
existing SSL algorithms in literature tend to regularize the model training by
perturbing networks and/or data. Observing that multi/dual-task learning
attends to various levels of information which have inherent prediction
perturbation, we ask the question in this work: can we explicitly build
task-level regularization rather than implicitly constructing networks- and/or
data-level perturbation-and-transformation for SSL? To answer this question, we
propose a novel dual-task-consistency semi-supervised framework for the first
time. Concretely, we use a dual-task deep network that jointly predicts a
pixel-wise segmentation map and a geometry-aware level set representation of
the target. The level set representation is converted to an approximated
segmentation map through a differentiable task transform layer. Simultaneously,
we introduce a dual-task consistency regularization between the level
set-derived segmentation maps and directly predicted segmentation maps for both
labeled and unlabeled data. Extensive experiments on two public datasets show
that our method can largely improve the performance by incorporating the
unlabeled data. Meanwhile, our framework outperforms the state-of-the-art
semi-supervised medical image segmentation methods. Code is available at:
this https URL

本文提出了一种双任务一致性半监督框架，以显式构建任务级别正则化而非隐式构建网络和 / 或数据层扰动 - 变换以实现医学图像分割的半监督学习，并通过两个公共数据集上的实验证明了其性能优于现有的半监督医学图像分割方法。