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

arXiv: https://arxiv.org/abs/1701.05013 · HTML: https://mlsanity.com/paper/1701.05013/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2017.

## Built on by (most cited first, 8 of 8)

- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [On the Generalization for Transfer Learning: An Information-Theoretic Analysis](https://mlsanity.com/paper/2207.05377/) (2022)
- [Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data](https://mlsanity.com/paper/2109.08311/) (2021)
- [JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets](https://mlsanity.com/paper/2105.00234/) (2021)
- [Domain generalization in deep learning for contrast-enhanced imaging](https://mlsanity.com/paper/2110.07360/) (2021)
- [Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism](https://mlsanity.com/paper/2210.16142/) (2022)
- [Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images](https://mlsanity.com/paper/2105.03068/) (2021)
- [Anatomical 3D Style Transfer Enabling Efficient Federated Learning with Extremely Low Communication Costs](https://mlsanity.com/paper/2410.20102/) (2024)

## Abstract (arXiv)

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