Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images

Autor: Jinming Dong, Tao Zhou, Yu Zhang, Xiaocong Chen, Lina Yao
Jazyk: angličtina
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
COVID-19 diagnosis
Computer Science - Machine Learning
Coronavirus disease 2019 (COVID-19)
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Few-shot learning
Chest ct
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Disease
Machine learning
computer.software_genre
01 natural sciences
Article
Machine Learning (cs.LG)
Artificial Intelligence
0103 physical sciences
FOS: Electrical engineering
electronic engineering
information engineering
0202 electrical engineering
electronic engineering
information engineering
010306 general physics
Health professionals
business.industry
Deep learning
Image and Video Processing (eess.IV)
Contrastive learning
Electrical Engineering and Systems Science - Image and Video Processing
Chest CT images
Feature (computer vision)
Signal Processing
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
computer
Software
Healthcare system
Zdroj: Pattern Recognition
Popis: 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. .
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.
Databáze: OpenAIRE
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