Abstract
Large-scale CT image studies often suffer from a lack of homogeneity regarding radiomic characteristics due to the images acquired with scanners from different vendors or with different reconstruction algorithms. We propose a deep learning-based framework called UDA-CT to tackle the homogeneity issue by leveraging both paired and unpaired images. Using UDA-CT, the CT images can be standardized both from different acquisition protocols of the same scanner and CT images acquired using a similar protocol but scanners from different vendors. UDA-CT incorporates recent advances in deep learning including domain adaptation and adversarial augmentation. It includes a unique design for model training batch which integrates nonstandard images and their adversarial variations to enhance model generalizability. The experimental results show that UDA-CT significantly improves the performance of the cross-scanner image standardization by utilizing both paired and unpaired data.
Original language | English |
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Title of host publication | Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 |
Editors | Donald Adjeroh, Qi Long, Xinghua Shi, Fei Guo, Xiaohua Hu, Srinivas Aluru, Giri Narasimhan, Jianxin Wang, Mingon Kang, Ananda M. Mondal, Jin Liu |
Pages | 1698-1701 |
Number of pages | 4 |
ISBN (Electronic) | 9781665468190 |
DOIs | |
State | Published - 2022 |
Event | 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 - Las Vegas, United States Duration: Dec 6 2022 → Dec 8 2022 |
Publication series
Name | Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 |
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Conference
Conference | 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 |
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Country/Territory | United States |
City | Las Vegas |
Period | 12/6/22 → 12/8/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Computed Tomography
- Domain Adaptation
- Generative Adversarial Network
- Image Synthesis
- Radiomics
ASJC Scopus subject areas
- Psychiatry and Mental health
- Information Systems and Management
- Biomedical Engineering
- Medicine (miscellaneous)
- Cardiology and Cardiovascular Medicine
- Health Informatics