We study pseudo labelling and its generalisation for semi-supervised
segmentation of medical images. Pseudo labelling has achieved great empirical
successes in semi-supervised learning, by utilising raw inferences on
unlabelled data as pseudo labels for self-training. In our paper, we build a
connection between pseudo labelling and the Expectation Maximization algorithm
which partially explains its empirical successes. We thereby realise that the
original pseudo labelling is an empirical estimation of its underlying full
formulation. Following this insight, we demonstrate the full generalisation of
pseudo labels under Bayes' principle, called Bayesian Pseudo Labels. We then
provide a variational approach to learn to approximate Bayesian Pseudo Labels,
by learning a threshold to select good quality pseudo labels. In the rest of
the paper, we demonstrate the applications of Pseudo Labelling and its
generalisation Bayesian Psuedo Labelling in semi-supervised segmentation of
medical images on: 1) 3D binary segmentation of lung vessels from CT volumes;
2) 2D multi class segmentation of brain tumours from MRI volumes; 3) 3D binary
segmentation of brain tumours from MRI volumes. We also show that pseudo labels
can enhance the robustness of the learnt representations.

该研究探讨了伪标记在半监督医学图像分割中的应用，提出了 Bayesian Pseudo Labels 这一伪标记的全面推广方法，并展示了该方法在三维肺血管、MRI 脑肿瘤和三维 MRI 脑肿瘤二元分割方面的应用。