@inbook{PB-VRVis-2022-042,
author = {Dietrichstein, Marc and Major, David and Trapp, Martin and Wimmer, Maria and Lenis, Dimitrios and Winter, Philip and Berg, Astrid and Neubauer, Theresa and B{\"u}hler, Katja},
title = {Anomaly Detection Using Generative Models and Sum-Product Networks in Mammography Scans},
year = {2022},
booktitle = {Deep Generative Models. DGM4MICCAI 2022. Lecture Notes in Computer Science, vol 13609.},
doi = {https://doi.org/10.1007/978-3-031-18576-2_8},
editor = {Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds.)},
url = {https://www.vrvis.at/publications/PB-VRVis-2022-042},
abstract = {Unsupervised anomaly detection models that are trained solely by healthy data, have gained importance in 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 = {Anomaly detection, Generative models, Sum-product networks, Mammography},
}