116 citations · 297 across the 6 of their papers we have counts for
13 papers
Contrastive Training for Improved Out-of-Distribution Detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy +10
Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investiga…
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
David Zimmerer, Jens Petersen, Simon A. A. Kohl +1
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 ano…
Reg R-CNN: Lesion Detection and Grading under Noisy Labels
Gregor N. Ramien, Paul F. Jaeger, Simon A. A. Kohl +1
For the task of concurrently detecting and categorizing objects, the medical imaging community commonly adopts methods developed on natural images. Current state-of-the-art object…
Unsupervised Anomaly Localization using Variational Auto-Encoders
David Zimmerer, Fabian Isensee, Jens Petersen +2
An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational A…
Deep Probabilistic Modeling of Glioma Growth
Jens Petersen, Paul F. Jäger, Fabian Isensee +8
Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the ass…
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection -- Short Paper
David Zimmerer, Simon Kohl, Jens Petersen +2
Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based autoencoders have shown great potential in detect…