Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression, and to predict lung cancer risk. However, visual assessment is time-consuming and subject to substantial inter-rater variability while standard densitometry approaches to quantify emphysema remain inferior to visual scoring. We explore if machine learning methods that learn from a large dataset of visually assessed CT scans can provide accurate estimates of emphysema extent and if methods that learn from emphysema extent scoring can outperform algorithms that learn only from emphysema presence scoring. Four Multiple Instance Learning classifiers, trained on emphysema presence labels, and five Learning with Label Proportions classifiers, trained on emphysema extent labels, are compared. Performance is evaluated on 600 low-dose CT scans from the Danish Lung Cancer Screening Trial and we find that learning from emphysema presence labels, which are much easier to obtain, gives equally good performance to learning from emphysema extent labels. The best performing Multiple Instance Learning and Learning with Label Proportions classifiers, achieve intra-class correlation coefficients around 0.90 and average overall agreement with raters of 78% and 79% compared to an inter-rater agreement of 83.

中文翻译：

---

学习量化肺气肿程度：我们需要什么标签？

准确评估肺气肿对于评估疾病严重程度和亚型，监测疾病进展并预测肺癌风险至关重要。但是，视觉评估非常耗时，并且在评估者之间存在很大差异，而用于量化肺气肿的标准光密度法仍然不及视觉评分。我们探讨了从大量视觉评估的CT扫描数据集中学习的机器学习方法是否可以提供对肺气肿程度的准确估计，以及从肺气肿程度评分中学习的方法是否能胜过仅从肺气肿存在性评分中学习的算法。比较了在肺气肿存在标签上训练的四个多实例学习分类器和在肺气肿程度标签上训练的五个带标签学习的分类器。在丹麦肺癌筛查试验的600次低剂量CT扫描中评估了性能，我们发现从肺气肿存在标签中学习非常容易获得，对从肺气肿程度标签中学习也具有同样好的性能。表现最佳的多实例学习和带有标签比例分类器的学习，在类内相关系数达到0.90左右时，评分者的平均总体同意率为78％和79％，而评分者间的同意率为83％。