2 citations · 2 across the 1 of their papers we have counts for
5 papers
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation
Kim-Celine Kahl, Carsten T. Lüth, Maximilian Zenk +2
Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodologi…
RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement
Gregor Koehler, Tassilo Wald, Constantin Ulrich +6
Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot…
Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection
Balint Kovacs, Nils Netzer, Michael Baumgartner +17
Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images. State-of-the-art computer-aided diagnosis…
Understanding Silent Failures in Medical Image Classification
Till J. Bungert, Levin Kobelke, Paul F. Jaeger
To ensure the reliable use of classification systems in medical applications, it is crucial to prevent silent failures. This can be achieved by either designing classifiers that ar…
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations
Silvia D. Almeida, Carsten T. Lüth, Tobias Norajitra +7
Classification of heterogeneous diseases is challenging due to their complexity, variability of symptoms and imaging findings. Chronic Obstructive Pulmonary Disease (COPD) is a pri…