Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation
This addresses the problem of limited labeled data in medical image segmentation, offering an incremental improvement over existing semi-supervised methods.
The paper tackles semi-supervised segmentation by proposing MisMatch, a framework that enforces consistency between predictions from dilated and eroded features, achieving state-of-the-art results on CT pulmonary vessel and MRI brain tumour segmentation tasks.
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.