This study investigates a curriculum-style strategy for semi-supervised CNN
segmentation, which devises a regression network to learn image-level
information such as the size of a target region. These regressions are used to
effectively regularize the segmentation network, constraining softmax
predictions of the unlabeled images to match the inferred label distributions.
Our framework is based on inequality constraints that tolerate uncertainties
with inferred knowledge, e.g., regressed region size, and can be employed for a
large variety of region attributes. We evaluated our proposed strategy for left
ventricle segmentation in magnetic resonance images (MRI), and compared it to
standard proposal-based semi-supervision strategies. Our strategy leverages
unlabeled data in more efficiently, and achieves very competitive results,
approaching the performance of full-supervision.

通过课程式策略和回归网络，基于不等式约束的半监督 CNN 分割技术在磁共振图像左心室分割方面的应用上，使用未标记的数据更高效地实现了分割网络，与标准的基于建议的半监督策略相比有着很有竞争力的结果，接近全监督的性能水平。