Self-supervised Mean Teacher for Semi-supervised Chest X-Ray Classification

Liu F, Tian Y, Cordeiro FR, Belagiannis V, Reid I, Carneiro G (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12966 LNCS

Pages Range: 426-436

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Virtual

ISBN: 9783030875886

DOI: 10.1007/978-3-030-87589-3_44

Abstract

The training of deep learning models generally requires a large amount of annotated data for effective convergence and generalisation. However, obtaining high-quality annotations is a laboursome and expensive process due to the need of expert radiologists for the labelling task. The study of semi-supervised learning in medical image analysis is then of crucial importance given that it is much less expensive to obtain unlabelled images than to acquire images labelled by expert radiologists. Essentially, semi-supervised methods leverage large sets of unlabelled data to enable better training convergence and generalisation than using only the small set of labelled images. In this paper, we propose Self-supervised Mean Teacher for Semi-supervised (S2 MTS2 ) learning that combines self-supervised mean-teacher pre-training with semi-supervised fine-tuning. The main innovation of S2 MTS2 is the self-supervised mean-teacher pre-training based on the joint contrastive learning, which uses an infinite number of pairs of positive query and key features to improve the mean-teacher representation. The model is then fine-tuned using the exponential moving average teacher framework trained with semi-supervised learning. We validate S2 MTS2 on the multi-label classification problems from Chest X-ray14 and CheXpert, and the multi-class classification from ISIC2018, where we show that it outperforms the previous SOTA semi-supervised learning methods by a large margin. Our code will be available upon paper acceptance.

Authors with CRIS profile

Vasileios Belagiannis

Involved external institutions

Rural Federal University of Pernambuco / Universidade Federal Rural de Pernambuco BR Brazil (BR) University of Adelaide AU Australia (AU) Universität Ulm DE Germany (DE)

How to cite

APA:

Liu, F., Tian, Y., Cordeiro, F.R., Belagiannis, V., Reid, I., & Carneiro, G. (2021). Self-supervised Mean Teacher for Semi-supervised Chest X-Ray Classification. In Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 426-436). Virtual: Springer Science and Business Media Deutschland GmbH.

MLA:

Liu, Fengbei, et al. "Self-supervised Mean Teacher for Semi-supervised Chest X-Ray Classification." Proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual Ed. Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan, Springer Science and Business Media Deutschland GmbH, 2021. 426-436.

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