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Classification of heterogeneous diseases is challenging due to theircomplexity, variability of symptoms and imaging findings. Chronic ObstructivePulmonary Disease (COPD) is a prime example, being underdiagnosed despite beingthe third leading cause of death. Its sparse, diffuse and heterogeneousappearance on computed tomography challenges supervised binary classification.We reformulate COPD binary classification as an anomaly detection task,proposing cOOpD: heterogeneous pathological regions are detected asOut-of-Distribution (OOD) from normal homogeneous lung regions. To this end, welearn representations of unlabeled lung regions employing a self-supervisedcontrastive pretext model, potentially capturing specific characteristics ofdiseased and healthy unlabeled regions. A generative model then learns thedistribution of healthy representations and identifies abnormalities (stemmingfrom COPD) as deviations. Patient-level scores are obtained by aggregatingregion OOD scores. We show that cOOpD achieves the best performance on twopublic datasets, with an increase of 8.2% and 7.7% in terms of AUROC comparedto the previous supervised state-of-the-art. Additionally, cOOpD yieldswell-interpretable spatial anomaly maps and patient-level scores which we showto be of additional value in identifying individuals in the early stage ofprogression. Experiments in artificially designed real-world prevalencesettings further support that anomaly detection is a powerful way of tacklingCOPD classification.

cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations