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Anomaly detection in MRI is of high clinical value in imaging and diagnosis.Unsupervised methods for anomaly detection provide interesting formulationsbased on reconstruction or latent embedding, offering a way to observeproperties related to factorization. We study four existing modeling methods,and report our empirical observations using simple data science tools, to seekoutcomes from the perspective of factorization as it would be most relevant tothe task of unsupervised anomaly detection, considering the case of brainstructural MRI. Our study indicates that anomaly detection algorithms thatexhibit factorization related properties are well capacitated with delineatorycapabilities to distinguish between normal and anomaly data. We have validatedour observations in multiple anomaly and normal datasets.

A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI