Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease.
IEEE Journal of Biomedical and Health Informatics
|July 11, 2018
Summary
This study shows Gaussian texture features improve automated chronic obstructive pulmonary disease (COPD) diagnosis across different medical imaging centers. A weighted classifier enhances accuracy for multicenter COPD detection using chest CT scans.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Pulmonology
Background:
- Chronic obstructive pulmonary disease (COPD) diagnosis relies on chest computed tomography (CT) scans.
- Automated COPD diagnosis using weakly supervised learning shows promise but lacks multicenter validation.
- Generalizability of existing classifiers across different scanners and protocols remains a challenge.
Purpose of the Study:
- To evaluate the generalizability of automated COPD classification methods across multiple centers and scanners.
- To investigate the effectiveness of Gaussian texture features and a weighted logistic classifier for multicenter COPD detection.
- To compare Gaussian texture features with intensity features for COPD classification in heterogeneous datasets.
Main Methods:
- Utilized a multicenter dataset of 803 chest CT scans from three centers and four scanners.
- Employed Gaussian texture features and a weighted logistic classifier, adjusting sample weights based on similarity to test data.
- Implemented a domain-discriminating classifier to further refine the weighting strategy for improved cross-domain performance.
Main Results:
- Gaussian texture features demonstrated superior performance compared to intensity features for multicenter COPD classification.
- The proposed weighted logistic classifier significantly improved classification accuracy across diverse scanning domains.
- The domain-discriminating weighting strategy further enhanced the robustness and generalizability of the COPD detection model.
Conclusions:
- Gaussian texture features combined with a weighted logistic classifier offer a robust approach for multicenter automated COPD diagnosis.
- The developed method shows improved generalizability across different imaging domains, addressing a key limitation in current automated systems.
- This research paves the way for more reliable and widely applicable AI-driven tools for COPD detection in clinical practice.
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