- 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.
- Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
- Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
- Paper Type: methodological development
- Primary Subject Area: Learning with Noisy Labels and Limited Data
- Secondary Subject Area: Unsupervised Learning and Representation Learning
- Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
- 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