论文标题
使用3D卷积神经网络中CT图像中的COPD分类
COPD Classification in CT Images Using a 3D Convolutional Neural Network
论文作者
论文摘要
慢性阻塞性肺疾病（COPD）是一种肺部疾病，并非完全可逆，是世界上发病和死亡的主要原因之一。提前检测和诊断COPD可以提高存活率并降低患者COPD进展的风险。当前，诊断COPD的主要检查工具是肺活量测定法。但是，计算机断层扫描（CT）用于检测COPD的症状和亚型分类。即使对于医生来说，使用不同的成像方式也是一项艰巨而乏味的任务，并且是对观察者间和观察者的主观变化。因此，开发可以自动对COPD与健康患者进行分类的甲基动物具有很大的兴趣。在本文中，我们提出了一种仅使用音量注释的3D深度学习方法来对COPD和肺气肿进行分类。我们还使用预先训练的COPD分类模型中的知识转移来证明转移学习对肺气肿的分类的影响。
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection and diagnosis of COPD can increase the survival rate and reduce the risk of COPD progression in patients. Currently, the primary examination tool to diagnose COPD is spirometry. However, computed tomography (CT) is used for detecting symptoms and sub-type classification of COPD. Using different imaging modalities is a difficult and tedious task even for physicians and is subjective to inter-and intra-observer variations. Hence, developing meth-ods that can automatically classify COPD versus healthy patients is of great interest. In this paper, we propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also demonstrate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.
