  Unsupervised pathology detection can be implemented by training a model on
healthy data only and measuring the deviation from the training set upon
inference, for example with CNN-based feature extraction and one-class
classifiers, or reconstruction-score-based methods such as AEs, GANs and
Diffusion models. Normalizing Flows (NF) have the ability to directly learn the
probability distribution of training examples through an invertible
architecture. We leverage this property in a novel 3D NF-based model named
CT-3DFlow, specifically tailored for patient-level pulmonary pathology
detection in chest CT data. Our model is trained unsupervised on healthy 3D
pulmonary CT patches, and detects deviations from its log-likelihood
distribution as anomalies. We aggregate patches-level likelihood values from a
patient's CT scan to provide a patient-level 'normal'/'abnormal' prediction.
Out-of-distribution detection performance is evaluated using expert annotations
on a separate chest CT test dataset, outperforming other state-of-the-art
methods.
