CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans

arXiv:2403.18514v1 Announce Type: cross
Abstract: 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 …

abstract arxiv class classifiers cnn cs.cv cs.lg data detection deviation eess.iv example extraction feature feature extraction gans inference measuring pathology scans set training type unsupervised