  Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity
and mortality. While COPD diagnosis is based on lung function tests, early
stages and progression of different aspects of the disease can be visible and
quantitatively assessed on computed tomography (CT) scans. Many studies have
been published that quantify imaging biomarkers related to COPD. In this paper
we present a convolutional neural network that directly computes visual
emphysema scores and predicts the outcome of lung function tests for 195 CT
scans from the COPDGene study. Contrary to previous work, the proposed method
does not encode any specific prior knowledge about what to quantify, but it is
trained end-to-end with a set of 1424 CT scans for which the output parameters
were available. The network provided state-of-the-art results for these tasks:
Visual emphysema scores are comparable to those assessed by trained human
observers; COPD diagnosis from estimated lung function reaches an area under
the ROC curve of 0.94, outperforming prior art. The method is easily
generalizable to other situations where information from whole scans needs to
be summarized in single quantities.
