Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images. (May 2021)

• Record Type: Journal Article
• Authors: Chen, Xiaocong; Yao, Lina; Zhou, Tao; Dong, Jinming; Zhang, Yu
• Is Part Of: Pattern recognition. Volume 113(2021)
• Publication Date: 2021-05
• Subjects: COVID-19 diagnosis -- Few-shot learning -- Contrastive learning -- Chest CT images; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.107826
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We formulate the COVID-19 diagnosis task as a few-shot learning problem. A self-supervised representation learning method is proposed to diagnose COVID-19 using only a limited number of samples. Our model is pre-trained on a general chest CT image dataset, andtested on two COVID-19 benchmarks. . Abstract: The current pandemic, caused by the outbreak of a novel coronavirus (COVID-19) in December 2019, has led to a global emergency that has significantly impacted economies, healthcare systems and personal wellbeing all around the world. Controlling the rapidly evolving disease requires highly sensitive and specific diagnostics. While RT-PCR is the most commonly used, it can take up to eight hours, and requires significant effort from healthcare professionals. As such, there is a critical need for a quick and automatic diagnostic system. Diagnosis from chest CT images is a promising direction. However, current studies are limited by the lack of sufficient training samples, as acquiring annotated CT images is time-consuming. To this end, we propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. Specifically, we use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets and adopt the prototypical network for classification. We validate the efficacy of the proposed model in comparison with other competing methods on two publicly available and annotated COVID-19 CT datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images.