Title: Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks Show Chinese title

Title: 使用深度神经网络对胸部计算机断层扫描图像进行肺气肿亚型分类

Abstract: Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society's visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52\%, outperforming a previously published method's accuracy of 45\%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes. Show Chinese abstract

Abstract: 准确识别肺气肿的亚型和严重程度对于慢性阻塞性肺病（COPD）的有效管理和疾病异质性的研究至关重要。手动分析肺气肿的亚型和严重程度既费时又主观。为了解决这一挑战，我们提出了一种基于深度学习的方法，用于自动化 Fleischner 学会的视觉评分系统，以实现肺气肿亚型分类和严重程度分析。我们使用 COPDGene 研究中的 9650 名受试者训练和评估了我们的算法。我们的算法预测准确率达到 52%，优于之前发表方法的 45% 准确率。此外，我们方法的预测分数与视觉评分之间的协议良好，而之前的方法仅获得中度协议。我们的方法采用回归训练策略生成类别标签，同时生成高分辨率的局部激活图以可视化网络预测。通过利用这些密集的激活图，我们的方法能够计算每个肺的肺气肿受累百分比，以及类别严重程度评分。此外，所提出的这种方法扩展了其预测能力，不仅限于中心叶间型肺气肿，还包括间隔旁型肺气肿亚型。