Xu, Mou-Cheng;
Zhou, Yukun;
Jin, Chen;
Groot, Marius de;
Alexander, Daniel C;
Oxtoby, Neil P;
Hu, Yipeng;
(2022)
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation.
In:
Proceedings of 25th International Conference on Medical Image Computing and Computer Assisted Intervention 2022.
Springer Nature: Cham, Switzerland.
Preview |
Text
2208.04435v1.pdf - Accepted Version Download (1MB) | Preview |
Abstract
This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we present a new formulation of pseudo-labelling as an Expectation-Maximization (EM) algorithm for clear statistical interpretation. Secondly, we propose a semi-supervised medical image segmentation method purely based on the original pseudo labelling, namely SegPL. We demonstrate SegPL is a competitive approach against state-of-the-art consistency regularisation based methods on semi-supervised segmentation on a 2D multi-class MRI brain tumour segmentation task and a 3D binary CT lung vessel segmentation task. The simplicity of SegPL allows less computational cost comparing to prior methods. Thirdly, we demonstrate that the effectiveness of SegPL may originate from its robustness against out-of-distribution noises and adversarial attacks. Lastly, under the EM framework, we introduce a probabilistic generalisation of SegPL via variational inference, which learns a dynamic threshold for pseudo labelling during the training. We show that SegPL with variational inference can perform uncertainty estimation on par with the gold-standard method Deep Ensemble .
Type: | Proceedings paper |
---|---|
Title: | Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation |
Event: | 25th International Conference on Medical Image Computing and Computer Assisted Intervention |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1007/978-3-031-16443-9_56 |
Publisher version: | https://doi.org/10.1007/978-3-031-16443-9_56 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
Keywords: | Semi-supervised segmentation, Pseudo labels, Expectation-maximization, Variational inference, Uncertainty, Probabilistic modelling, Out-of-distribution, Adversarial robustness |
UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10154343 |
Archive Staff Only
![]() |
View Item |