Anomaly Detection Using Generative Models and Sum-Product Networks in Mammography Scans

Deep Generative Models - 2nd MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Proceedings, pp. 77-86, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Volume 13609 LNCS

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.

Description

Funding Information: VRVis is funded by BMK, BMDW, Styria, SFG, Tyrol and Vienna Business Agency in the scope of COMET-Competence Centers for Excellent Technologies (879730) which is managed by FFG. Thanks go to AGFA HealthCare, project partner of VRVis, for providing valuable input. Martin Trapp acknowledges funding from the Academy of Finland (347279).

Keywords

Anomaly detection, Generative models, Mammography, Sum-product networks

DOI

10.1007/978-3-031-18576-2_8

Citation

Dietrichstein, M, Major, D, Trapp, M, Wimmer, M, Lenis, D, Winter, P, Berg, A, Neubauer, T & Bühler, K 2022, Anomaly Detection Using Generative Models and Sum-Product Networks in Mammography Scans . in A Mukhopadhyay, I Oksuz, S Engelhardt, D Zhu & Y Yuan (eds), Deep Generative Models - 2nd MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13609 LNCS, Springer, pp. 77-86, Workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention, Singapore, Singapore, 22/09/2022 . https://doi.org/10.1007/978-3-031-18576-2_8

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202212227229

Collections

[article-cris] Perustieteiden korkeakoulu / SCI

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Anomaly Detection Using Generative Models and Sum-Product Networks in Mammography Scans

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