Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation

Moucheng Xu, Yukun Zhou, Chen Jin, Stefano B Blumberg, Frederick Wilson, Marius De Groot, Daniel C. Alexander, Neil Oxtoby, Joseph Jacob

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

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/learning-morphological-feature-perturbations/code)