Medical image segmentation, which is essential for many clinical
applications, has achieved almost human-level performance via data-driven deep
learning techniques. Nevertheless, its performance is predicated on the costly
process of manually annotating a large amount of medical images. To this end,
we propose a novel framework for robust semi-supervised medical image
segmentation using diagonal hierarchical consistency (DiHC-Net). First, it is
composed of multiple sub-models with identical multi-scale architecture but
with distinct sub-layers, such as up-sampling and normalisation layers. Second,
a novel diagonal hierarchical consistency is enforced between one model's
intermediate and final prediction and other models' soft pseudo labels in a
diagonal hierarchical fashion. Experimental results verify the efficacy of our
simple framework, outperforming all previous approaches on public Left Atrium
(LA) dataset.

通过使用对角层次一致性（DiHC-Net）的鲁棒半监督医学图像分割新框架，实现了几乎达到人类水平的临床应用中的医学图像分割结果，且在公开的左心房（LA）数据集上表现优于以往的方法。