Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detecting anomalies in medical images. However, state-of-the-art anomaly scores are still based on the reconstruction error, which lacks in two essential parts: it ignores the model-internal representation employed for reconstruction, and it lacks formal assertions and comparability between samples. We address these shortcomings by proposing the Context-encoding Variational Autoencoder (ceVAE) which combines reconstruction- with density-based anomaly scoring. This improves the sample- as well as pixel-wise results. In our experiments on the BraTS-2017 and ISLES-2015 segmentation benchmarks, the ceVAE achieves unsupervised ROC-AUCs of 0.95 and 0.89, respectively, thus outperforming state-of-the-art methods by a considerable margin.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionUnsupervised Anomaly DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention
In the age of big data, time series are being generated in massive amounts. In the energy field, smart grids are enabling a unprecedented data acquisition with the integration of sensors and smart devices. In the context…
Anomaly DetectionDeep AttentionRepresentation LearningTime Series+2Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder
In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly detection task on a general object represen…
Anomaly DetectionUnsupervised Anomaly DetectionChallenges for Unsupervised Anomaly Detection in Particle Physics
Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use autoencoders, which rely on the ability to…
Anomaly DetectionUnsupervised Anomaly DetectionStRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder
Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models…
Anomaly DetectionUnsupervised Anomaly DetectionA Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions. Previous work on deep-learning-based anomaly detection has primarily focused on …
Anomaly Detection