Keywords: Attention, Semi-Supervised, Segmentation, Representation, Consistency-Regularisation, Feature Augmentation, End-To-End
TL;DR: We propose attention guided morphological feature perturbations for consistency-driven semi-supervised segmentation of medical images.
Abstract: In this paper, 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 decoders. One decoder learns positive attention to the foreground regions of interest (RoI) on unlabelled images thereby generating dilated features of the foreground. The other decoder learns negative attention to the foreground on the same unlabelled images thereby generating eroded features of the foreground. 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. We also show that the effectiveness of MisMatch comes from better model calibration than its supervised learning counterpart.
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Paper Type: methodological development
Primary Subject Area: Learning with Noisy Labels and Limited Data
Secondary Subject Area: Unsupervised Learning and Representation Learning
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Code And Data: code: https://github.com/moucheng2017/Morphological_Feature_Perturbation_SSL carve data: https://arteryvein.grand-challenge.org/ brats data: https://www.med.upenn.edu/sbia/brats2018/data.html