Transfer learning for multi-center classification of chronic obstructive pulmonary disease

Abstract

Chronic obstructive pulmonary disease (COPD) is a lung disease which can be quantified using chest computed tomography (CT) 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 multi-center 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 multi-center 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 classification of COPD, upon acceptance of the paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copd

Accepted at Journal of Biomedical and Health Informatics

References in corpus (4)

• Multiple Instance Learning with Bag Dissimilarities
• DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images
• Classification of COPD with Multiple Instance Learning
• Label Stability in Multiple Instance Learning

Cited by in corpus (8)

• Domain Adaptation for Medical Image Analysis: A Survey
• A review of domain adaptation without target labels
• Domain generalization in deep learning for contrast-enhanced imaging
• Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism
• Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data
• Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation
• Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images
• Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector