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

• URL: http://arxiv.org/abs/2210.06188v1
• Date: Wed, 12 Oct 2022 13:18:16 GMT
• Title: Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans
• Authors: Marc Dietrichstein, David Major, Maria Wimmer, Dimitrios Lenis, Philip Winter, Astrid Berg, Theresa Neubauer, Katja B\"uhler
• Abstract summary: Autoencoders and generative adversarial networks are the standard anomaly detection methods. We propose a novel combination of generative models and a graphical model. We observe superior performance over utilizing the models standalone and state-of-the-art in anomaly detection for medical data.
• Score: 2.2515303891664358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• Anomaly Detection with Ensemble of Encoder and Decoder [2.8199078343161266]
  Anomaly detection in power grids aims to detect and discriminate anomalies caused by cyber attacks against the power system. We propose a novel anomaly detection method by modeling the data distribution of normal samples via multiple encoders and decoders. Experiment results on network intrusion and power system datasets demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-03-11T15:49:29Z)
• Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection [64.1680666036655]
  We revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view. We have evaluated the mathematical model using two different datasets.
  arXiv  Detail & Related papers  (2023-02-02T13:41:18Z)
• Detecting Anomalies using Generative Adversarial Networks on Images [0.0]
  This paper proposes a novel Generative Adversarial Network (GAN) based model for anomaly detection. It uses normal (non-anomalous) images to learn about the normality based on which it detects if an input image contains an anomalous/threat object. Experiments are performed on three datasets, viz. CIFAR-10, MVTec AD (for industrial applications) and SIXray (for X-ray baggage security)
  arXiv  Detail & Related papers  (2022-11-24T21:52:25Z)
• Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models [1.6352599467675781]
  We propose a method based on diffusion models to detect and segment anomalies in brain imaging. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data.
  arXiv  Detail & Related papers  (2022-06-07T17:30:43Z)
• on the effectiveness of generative adversarial network on anomaly detection [1.6244541005112747]
  GANs rely on the rich contextual information of these models to identify the actual training distribution. We suggest a new unsupervised model based on GANs --a combination of an autoencoder and a GAN. A new scoring function was introduced to target anomalies where a linear combination of the internal representation of the discriminator and the generator's visual representation, plus the encoded representation of the autoencoder, come together to define the proposed anomaly score.
  arXiv  Detail & Related papers  (2021-12-31T16:35:47Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Image Anomaly Detection by Aggregating Deep Pyramidal Representations [16.246831343527052]
  Anomaly detection consists in identifying, within a dataset, those samples that significantly differ from the majority of the data. This paper focuses on image anomaly detection using a deep neural network with multiple pyramid levels to analyze the image features at different scales.
  arXiv  Detail & Related papers  (2020-11-12T09:58:27Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.