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

arXiv: https://arxiv.org/abs/2210.06188 · HTML: https://mlsanity.com/paper/2210.06188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits](https://mlsanity.com/paper/2004.06231/) (2020)
- [Unsupervised Anomaly Localization using Variational Auto-Encoders](https://mlsanity.com/paper/1907.02796/) (2019)
- [Anomaly detection through latent space restoration using vector-quantized variational autoencoders](https://mlsanity.com/paper/2012.06765/) (2020)
- [Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT](https://mlsanity.com/paper/1905.12806/) (2019)
- [Bayesian Learning of Sum-Product Networks](https://mlsanity.com/paper/1905.10884/) (2019)
- [Unsupervised 3D Brain Anomaly Detection](https://mlsanity.com/paper/2010.04717/) (2020)
- [Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks](https://mlsanity.com/paper/2108.02233/) (2021)

## Built on by (most cited first, 2 of 2)

- [Sparse Probabilistic Circuits via Pruning and Growing](https://mlsanity.com/paper/2211.12551/) (2022)
- [Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection](https://mlsanity.com/paper/2403.02307/) (2024)

## Abstract (arXiv)

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.