11 citations · 19 across the 4 of their papers we have counts for
8 papers
Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging
Max-Heinrich Laves, Sontje Ihler, Jacob F. Fast +2
The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variat…
Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer
Sontje Ihler, Max-Heinrich Laves, Tobias Ortmaier
Fast motion feedback is crucial in computer-aided surgery (CAS) on moving tissue. Image-assistance in safety-critical vision applications requires a dense tracking of tissue motion…
Calibration of Model Uncertainty for Dropout Variational Inference
Max-Heinrich Laves, Sontje Ihler, Karl-Philipp Kortmann +1
The model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. In this paper, different logit scaling methods are extended to…
Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference
Max-Heinrich Laves, Sontje Ihler, Karl-Philipp Kortmann +1
Model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. The uncertainty does not represent the model error well. In this p…
Uncertainty Quantification in Computer-Aided Diagnosis: Make Your Model say "I don't know" for Ambiguous Cases
Max-Heinrich Laves, Sontje Ihler, Tobias Ortmaier
We evaluate two different methods for the integration of prediction uncertainty into diagnostic image classifiers to increase patient safety in deep learning. In the first method,…
Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior
Max-Heinrich Laves, Sontje Ihler, Tobias Ortmaier
We present deformable unsupervised medical image registration using a randomly-initialized deep convolutional neural network (CNN) as regularization prior. Conventional registratio…