  We propose MisMatch, a novel consistency-driven semi-supervised segmentation
framework which produces predictions that are invariant to learnt feature
perturbations. MisMatch consists of an encoder and a two-head decoders. One
decoder learns positive attention to the foreground regions of interest (RoI)
on unlabelled images thereby generating dilated features. The other decoder
learns negative attention to the foreground on the same unlabelled images
thereby generating eroded features. We then apply a consistency regularisation
on the paired predictions. MisMatch outperforms state-of-the-art
semi-supervised methods on a CT-based pulmonary vessel segmentation task and a
MRI-based brain tumour segmentation task. In addition, we show that the
effectiveness of MisMatch comes from better model calibration than its
supervised learning counterpart.
