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MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels
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
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