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2021 | OriginalPaper | Chapter

Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-Ray Images

Authors : Xiao Qi, David J. Foran, John L. Nosher, Ilker Hacihaliloglu

Published in: Machine Learning in Medical Imaging

Publisher: Springer International Publishing

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Abstract

Computed tomography (CT) and chest X-ray (CXR) have been the two dominant imaging modalities deployed for improved management of Coronavirus disease 2019 (COVID-19). Due to faster imaging, less radiation exposure, and being cost-effective CXR is preferred over CT. However, the interpretation of CXR images, compared to CT, is more challenging due to low image resolution and COVID-19 image features being similar to regular pneumonia. Computer-aided diagnosis via deep learning has been investigated to help mitigate these problems and help clinicians during the decision-making process. The requirement for a large amount of labeled data is one of the major problems of deep learning methods when deployed in the medical domain. To provide a solution to this, in this work, we propose a semi-supervised learning (SSL) approach using minimal data for training. We integrate local-phase CXR image features into a multi-feature convolutional neural network architecture where the training of SSL method is obtained with a teacher/student paradigm. Quantitative evaluation is performed on 8,851 normal (healthy), 6,045 pneumonia, and 3,795 COVID-19 CXR scans. By only using 7.06% labeled and 16.48% unlabeled data for training, 5.53% for validation, our method achieves 93.61% mean accuracy on a large-scale (70.93%) test data. We provide comparison results against fully supervised and SSL methods. The code and dataset will be made available after acceptance.

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Metadata
Title
Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-Ray Images
Authors
Xiao Qi
David J. Foran
John L. Nosher
Ilker Hacihaliloglu
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-87589-3_16

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