Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans
Abstract
Unsupervised anomaly detection models which are trained solely by healthy data, have gained importance in the recent years, as the annotation of medical data is a tedious task. Autoencoders and generative adversarial networks are the standard anomaly detection methods that are utilized to learn the data distribution. However, they fall short when it comes to inference and evaluation of the likelihood of test samples. We propose a novel combination of generative models and a probabilistic graphical model. After encoding image samples by autoencoders, the distribution of data is modeled by Random and Tensorized Sum-Product Networks ensuring exact and efficient inference at test time. We evaluate different autoencoder architectures in combination with Random and Tensorized Sum-Product Networks on mammography images using patch-wise processing and observe superior performance over utilizing the models standalone and state-of-the-art in anomaly detection for medical data.
Keywords
Cite
@article{arxiv.2210.06188,
title = {Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans},
author = {Marc Dietrichstein and David Major and Martin Trapp and Maria Wimmer and Dimitrios Lenis and Philip Winter and Astrid Berg and Theresa Neubauer and Katja Bühler},
journal= {arXiv preprint arXiv:2210.06188},
year = {2022}
}
Comments
Submitted to DGM4MICCAI 2022 Workshop. This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this contribution is published in LNCS 13609, and is available online at https://doi.org/10.1007/978-3-031-18576-2_8