Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci.
Code (18)
Tasks
Anomaly DetectionGenerative Adversarial NetworkUnsupervised Anomaly DetectionSimilar Papers 제목 키워드 기반
Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks
We propose a novel unsupervised anomaly detection approach using generative adversarial networks and SOP-derived spectrograms. Demonstrating remarkable efficacy, our method achieves over 97% accuracy on SOP datasets from…
Anomaly DetectionUnsupervised Anomaly DetectionDouble-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators
Anomaly detection is a challenging task for machine learning algorithms due to the inherent class imbalance. It is costly and time-demanding to manually analyse the observed data, thus usually only few known anomalies if…
Anomaly DetectionBIG-bench Machine LearningUnsupervised Anomaly DetectionAdversarial Denoising Diffusion Model for Unsupervised Anomaly Detection
In this paper, we propose the Adversarial Denoising Diffusion Model (ADDM). The ADDM is based on the Denoising Diffusion Probabilistic Model (DDPM) but complementarily trained by adversarial learning. The proposed advers…
Anomaly DetectionDenoisingmodelUnsupervised Anomaly DetectionUnsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review
Unsupervised anomaly detection (UAD) based on deep generative modelling has been increasingly explored for identifying pathological brain abnormalities without requiring voxel-level annotations. By learning the distribut…
Unsupervised Anomaly DetectionTAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
Anomaly detection in time series data is a significant problem faced in many application areas such as manufacturing, medical imaging and cyber-security. Recently, Generative Adversarial Networks (GAN) have gained attent…
Anomaly DetectionTime SeriesTime Series AnalysisTime Series Anomaly Detection