ArticleIEEE journal of biomedical and health informatics2018
Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease.
Article in IEEE journal of biomedical and health informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Multimodal multi-instance learning for cardiopulmonary exercise testing performance prediction.NPJ digital medicine · 2026Article
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- Oxidative Stress and Inflammation in Hypoxemic Respiratory Diseases and Their Comorbidities: Molecular Insights and Diagnostic Advances in Chronic Obstructive Pulmonary Disease and Sleep Apnea.Antioxidants (Basel, Switzerland) · 2025Review
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images.BMC pulmonary medicine · 2024Article
- Correlation enhanced distribution adaptation for prediction of fall risk.Scientific reports · 2024Article
- Application and Prospects of Artificial Intelligence Technology in Early Screening of Chronic Obstructive Pulmonary Disease at Primary Healthcare Institutions in China.International journal of chronic obstructive pulmonary disease · 2024Review
- COMMUTE: Communication-efficient transfer learning for multi-site risk prediction.Journal of biomedical informatics · 2023Article
- Early detection of COPD based on graph convolutional network and small and weakly labeled data.Medical & biological engineering & computing · 2022Article
- A Multilevel Transfer Learning Technique and LSTM Framework for Generating Medical Captions for Limited CT and DBT Images.Journal of digital imaging · 2022Article
- Multiple instance learning for lung pathophysiological findings detection using CT scans.Medical & biological engineering & computing · 2022Article
- Domain Adaptation for Medical Image Analysis: A Survey.IEEE transactions on bio-medical engineering · 2022Review
- A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation.Frontiers in neuroinformatics · 2022Article
- DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images.Computer methods and programs in biomedicine · 2021Article
- Deep learning for predicting COVID-19 malignant progression.Medical image analysis · 2021Article
- Augmenting Transfer Learning with Feature Extraction Techniques for Limited Breast Imaging Datasets.Journal of digital imaging · 2021Article
- A Comparative Evaluation of Computed Tomography Images for the Classification of Spirometric Severity of the Chronic Obstructive Pulmonary Disease with Deep Learning.Diagnostics (Basel, Switzerland) · 2021Article
- Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation.Computer methods and programs in biomedicine · 2021Article
- Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning.Journal of digital imaging · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Chronic obstructive pulmonary disease (COPD) is a lung disease that can be quantified using chest computed tomography scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multicenter dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multicenter classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains can further improve the results. To encourage further research into transfer learning methods for the classification of COPD, upon acceptance of this paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copd.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.