Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images

Calderon-Ramirez, Saul; Giri, Raghvendra; Moemeni, Armaghan; Uma�a, Mario; Elizondo, David; Torrents-Barrena, Jordina; Molina-Cabello, Miguel A

Abstract

Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently available tests are scarce and limited to people in danger of serious illness. The application of deep learning to chest X-ray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed. We developed and tested a semi-supervised deep learning framework based on the Mix Match architecture to classify chest X-rays into Covid-19, pneumonia and healthy cases. The presented approach was calibrated using two publicly available datasets. The results show an accuracy increase of around 15% under low labelled / unlabelled data ratio. This indicates that our semi-supervised framework can help improve performance levels towards Covid-19 detection when the amount of high-quality labelled data is scarce. Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison.

Citation

Calderon-Ramirez, S., Giri, R., Moemeni, A., Umaña, M., Elizondo, D., Torrents-Barrena, J., & Molina-Cabello, M. A. (2021). Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images. In Proceedings of ICPR 2020 : 25th International Conference on Pattern Recognition. https://doi.org/10.1109/ICPR48806.2021.9412946

• Conference Name: 25th International Conference on Pattern Recognition (ICPR 2020)
• Conference Location: Milan, Italy
• Start Date: Jan 10, 2021
• End Date: Jan 15, 2021
• Acceptance Date: Jun 20, 2020
• Online Publication Date: May 5, 2021
• Publication Date: Jan 10, 2021
• Deposit Date: Jun 24, 2020
• Publicly Available Date: Jan 10, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: Proceedings of ICPR 2020 : 25th International Conference on Pattern Recognition
• DOI: https://doi.org/10.1109/ICPR48806.2021.9412946
• Keywords: Semi-supervised Deep Learning; Mix Match; Chest X-Ray; Covid-19; Computer Aided Diagnosis
• Public URL: https://nottingham-repository.worktribe.com/output/4704400
• Publisher URL: https://ieeexplore.ieee.org/document/9412946
• Related Public URLs: https://www.micc.unifi.it/icpr2020/
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