  Chronic obstructive pulmonary disease (COPD) is a lung disease where early
detection benefits the survival rate. COPD can be quantified by classifying
patches of computed tomography images, and combining patch labels into an
overall diagnosis for the image. As labeled patches are often not available,
image labels are propagated to the patches, incorrectly labeling healthy
patches in COPD patients as being affected by the disease. We approach
quantification of COPD from lung images as a multiple instance learning (MIL)
problem, which is more suitable for such weakly labeled data. We investigate
various MIL assumptions in the context of COPD and show that although a concept
region with COPD-related disease patterns is present, considering the whole
distribution of lung tissue patches improves the performance. The best method
is based on averaging instances and obtains an AUC of 0.742, which is higher
than the previously reported best of 0.713 on the same dataset. Using the full
training set further increases performance to 0.776, which is significantly
higher (DeLong test) than previous results.
