CT2CXR: CT-based CXR Synthesis for Covid-19 Pneumonia Classification
13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022
; 13583 LNCS:210-219, 2022.
Article
in English
| Scopus | ID: covidwho-2173784
ABSTRACT
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. © 2022, Springer Nature Switzerland AG.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022
Year:
2022
Document Type:
Article
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