  We propose an end-to-end deep learning method that learns to estimate
emphysema extent from proportions of the diseased tissue. These proportions
were visually estimated by experts using a standard grading system, in which
grades correspond to intervals (label example: 1-5% of diseased tissue). The
proposed architecture encodes the knowledge that the labels represent a
volumetric proportion. A custom loss is designed to learn with intervals. Thus,
during training, our network learns to segment the diseased tissue such that
its proportions fit the ground truth intervals. Our architecture and loss
combined improve the performance substantially (8% ICC) compared to a more
conventional regression network. We outperform traditional lung densitometry
and two recently published methods for emphysema quantification by a large
margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance.
Moreover, our method generates emphysema segmentations that predict the spatial
distribution of emphysema at human level.
