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

## TL;DR

This study explores transfer learning techniques to improve the multi-center classification of COPD from chest CT scans, demonstrating that domain-aware weighting enhances classifier performance across diverse datasets.

## Contribution

It introduces a transfer learning approach using Gaussian texture features and a domain-discrimination weighting strategy for better multi-center COPD classification.

## Key findings

- Gaussian texture features outperform intensity features
- Domain-based weighting improves classification accuracy
- Multi-center dataset evaluation confirms method robustness

## 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