Search Results for author:
Found 14 papers, 7 papers with code
Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging
We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show that predictive uncertainty is systematically underestimated.
Uncertainty Calibration Error: A New Metric for Multi-Class Classification
Various metrics have recently been proposed to measure uncertainty calibration of deep models for classification.
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
We use a randomly initialized convolutional network as parameterization of the reconstructed image and perform gradient descent to match the observation, which is known as deep image prior.
Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer
Using flow estimations from teacher model FlowNet2 we specialize a fast student model FlowNet2S on the patient-specific domain.
Calibration of Model Uncertainty for Dropout Variational Inference
The model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration.
Well-Calibrated Regression Uncertainty in Medical Imaging with Deep Learning
The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance.
Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference
Model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration.
Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior
Our approach uses the idea of deep image priors to combine convolutional networks with conventional registration methods based on manually engineered priors.
Uncertainty Quantification in Computer-Aided Diagnosis: Make Your Model say "I don't know" for Ambiguous Cases
We evaluate two different methods for the integration of prediction uncertainty into diagnostic image classifiers to increase patient safety in deep learning.
Semantic denoising autoencoders for retinal optical coherence tomography
Noise in speckle-prone optical coherence tomography tends to obfuscate important details necessary for medical diagnosis.
Retinal OCT disease classification with variational autoencoder regularization
A recent study established a diagnostic tool based on convolutional neural networks (CNN), which was trained on a large database of retinal OCT images.
Endoscopic vs. volumetric OCT imaging of mastoid bone structure for pose estimation in minimally invasive cochlear implant surgery
The resulting volumentric images provide additional information on the shape of caveties in the bone structure, which will be useful for image-to-patient registration and to estimate the drill trajectory.
Deep learning based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography
Subsequent to this, the projections are warped by predicted lateral flow and 1D depth flow is estimated.
A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation
Methods Four machine learning based methods SegNet, UNet, ENet and ErfNet were trained with supervision on a novel 7-class dataset of the human larynx.
