  Classification of heterogeneous diseases is challenging due to their
complexity, variability of symptoms and imaging findings. Chronic Obstructive
Pulmonary Disease (COPD) is a prime example, being underdiagnosed despite being
the third leading cause of death. Its sparse, diffuse and heterogeneous
appearance on computed tomography challenges supervised binary classification.
We reformulate COPD binary classification as an anomaly detection task,
proposing cOOpD: heterogeneous pathological regions are detected as
Out-of-Distribution (OOD) from normal homogeneous lung regions. To this end, we
learn representations of unlabeled lung regions employing a self-supervised
contrastive pretext model, potentially capturing specific characteristics of
diseased and healthy unlabeled regions. A generative model then learns the
distribution of healthy representations and identifies abnormalities (stemming
from COPD) as deviations. Patient-level scores are obtained by aggregating
region OOD scores. We show that cOOpD achieves the best performance on two
public datasets, with an increase of 8.2% and 7.7% in terms of AUROC compared
to the previous supervised state-of-the-art. Additionally, cOOpD yields
well-interpretable spatial anomaly maps and patient-level scores which we show
to be of additional value in identifying individuals in the early stage of
progression. Experiments in artificially designed real-world prevalence
settings further support that anomaly detection is a powerful way of tackling
COPD classification.
