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

Anomaly detection in MRI is of high clinical value in imaging and diagnosis. Unsupervised methods for anomaly detection provide interesting formulations based on reconstruction or latent embedding, offering a way to observe properties related to factorization. We study four existing modeling methods...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-11
Main Authors: Das, Ayantika, Palla, Arun, Keerthi Ram, Sivaprakasam, Mohanasankar
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Brain
Data science
Factorization
Magnetic resonance imaging
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Anomaly detection in MRI is of high clinical value in imaging and diagnosis. Unsupervised methods for anomaly detection provide interesting formulations based on reconstruction or latent embedding, offering a way to observe properties related to factorization. We study four existing modeling methods, and report our empirical observations using simple data science tools, to seek outcomes from the perspective of factorization as it would be most relevant to the task of unsupervised anomaly detection, considering the case of brain structural MRI. Our study indicates that anomaly detection algorithms that exhibit factorization related properties are well capacitated with delineatory capabilities to distinguish between normal and anomaly data. We have validated our observations in multiple anomaly and normal datasets.
ISSN:2331-8422