CT2CXR: CT-based CXR Synthesis for Covid-19 Pneumonia Classification

Page view(s)
Checked on Jan 21, 2024
CT2CXR: CT-based CXR Synthesis for Covid-19 Pneumonia Classification
CT2CXR: CT-based CXR Synthesis for Covid-19 Pneumonia Classification
Journal Title:
Machine Learning in Medical Imaging
Publication Date:
15 December 2022
Yuen, P. H. H., Wang, X., Lin, Z., Chow, N. K. W., Cheng, J., Tan, C. H., & Huang, W. (2022). CT2CXR: CT-based CXR Synthesis for Covid-19 Pneumonia Classification. Lecture Notes in Computer Science, 210–219. https://doi.org/10.1007/978-3-031-21014-3_22
Chest X-ray (CXR) is a common imaging modality for examination of pneumonia. However, some pneumonia signs which are visible in CT may not be clearly identifiable in CXR. It is challenging to create a good ground truth for positive pneumonia cases based on CXR images especially for cases with small pneumonia lesions. In this paper, we propose a novel CT-based CXR synthesis framework, called ct2cxr, to perform data augmentation for pneumonia classification. Generative Adversarial Networks (GANs) were exploited and a customized loss function was proposed for model training to preserve the target pathology and maintain high image fidelity. Our results show that CXR images generated through style mixing can enhance the performance of general pneumonia classification models. Testing the models on a Covid-19 dataset shows similar improvements over the baseline models.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - GAP
Grant Reference no. : ACCL/19-GAP012-R20H
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-21014-3_22
Files uploaded: