IEEE J Biomed Health Inform. 2018 Sep;22(5):1486-1496. doi: 10.1109/JBHI.2017.2769800. Epub 2017 Nov 3.
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.
慢性阻塞性肺疾病（COPD）是一种可以通过胸部计算机断层扫描进行量化的肺部疾病。最近的研究表明，COPD 可以使用强度和纹理分布的弱监督学习自动诊断。然而，到目前为止，此类分类器仅在来自单一域的扫描上进行了评估，并且不清楚它们是否可以跨域（例如不同的扫描仪或扫描协议）进行泛化。为了解决这个问题，我们在一个多中心数据集（来自三个不同中心的总共 803 个扫描）中研究了 COPD 的分类，该数据集具有不同的扫描仪和异构的受试者分布。我们的方法基于高斯纹理特征和加权逻辑回归分类器，该分类器增加了与测试数据相似的样本的权重。我们表明，高斯纹理特征优于以前在多中心分类任务中使用的强度特征。我们还表明，基于针对不同域的扫描进行区分训练的分类器的加权策略可以进一步提高结果。为了鼓励进一步研究 COPD 分类的迁移学习方法，本文接受后，我们将在 http://bigr.nl/research/projects/copd 上发布本研究中使用的两个特征数据集。
