Semi-supervised learning (SSL) is a promising machine learning paradigm to address the issue of label scarcity in medical imaging. SSL methods were originally developed in image classification. The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations. However, image level perturbations violate the cluster assumption in the setting of segmentation. Moreover, existing image level perturbations are hand-crafted which could be sub-optimal. Therefore, it is a not trivial to straightforwardly adapt existing SSL image classification methods in segmentation. In this paper, we propose MisMatch, a semi-supervised segmentation framework based on the consistency between paired predictions which are derived from two differently learnt morphological feature perturbations. MisMatch consists of an encoder and two decoders. One decoder learns positive attention for foreground on unlabelled data thereby generating dilated features of foreground. The other decoder learns negative attention for foreground on the same unlabelled data thereby generating eroded features of foreground. We first develop a 2D U-net based MisMatch framework and perform extensive cross-validation on a CT-based pulmonary vessel segmentation task and show that MisMatch statistically outperforms state-of-the-art semi-supervised methods when only 6.25\% of the total labels are used. In a second experiment, we show that U-net based MisMatch outperforms state-of-the-art methods on an MRI-based brain tumour segmentation task. In a third experiment, we show that a 3D MisMatch outperforms a previous method using input level augmentations, on a left atrium segmentation task. Lastly, we find that the performance improvement of MisMatch over the baseline might originate from its better calibration.

翻译：半监督学习（SSL）是解决医学成像中标签稀缺问题的有前途机器学习范式。SSL方法最初在图像分类中发展。图像分类中最先进的SSL方法利用一致性正则化学习对输入级扰动不变的未标注预测。然而，图像级扰动违反了分割设置中的聚类假设。此外，现有图像级扰动是人工设计的，可能并非最优。因此，直接将现有SSL图像分类方法适配到分割任务中并非易事。本文提出MisMatch——一种基于配对预测一致性的半监督分割框架，该预测源自两种不同学习的形态特征扰动。MisMatch包含一个编码器和两个解码器。一个解码器学习对未标注数据的前景进行正注意力，从而生成前景的膨胀特征；另一个解码器对同一未标注数据的前景进行负注意力，从而生成前景的腐蚀特征。我们首先开发了基于2D U-net的MisMatch框架，并在CT肺部血管分割任务上进行广泛交叉验证，结果表明当仅使用总标签的6.25%时，MisMatch在统计上优于最先进的半监督方法。在第二个实验中，我们展示了基于U-net的MisMatch在MRI脑肿瘤分割任务上优于最先进方法。在第三个实验中，我们展示了3D MisMatch在左心房分割任务上优于使用输入级增强的先前方法。最后，我们发现MisMatch相较于基线的性能提升可能源于其更好的校准。