  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 and 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. We further investigate if
machine learning algorithms that learn from a scoring of emphysema extent can
outperform algorithms that learn only from a scoring of emphysema presence. We
compare four Multiple Instance Learning classifiers that are trained on
emphysema presence labels, and five Learning with Label Proportions classifiers
that are trained on emphysema extent labels. We evaluate performance on 600
low-dose CT scans from the Danish Lung Cancer Screening Trial and 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
classifiers achieve intra-class correlation coefficients around 0.90 and
average overall agreement with raters of 78% and 79% on six emphysema extent
classes versus inter-rater agreement of 83%.
