Search Results for author: Tobias Ortmaier

Found 14 papers, 7 papers with code

Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging

1 code implementation26 Apr 2021 Max-Heinrich Laves, Sontje Ihler, Jacob F. Fast, Lüder A. Kahrs, Tobias Ortmaier

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.

Bayesian Inference regression

Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior

1 code implementation20 Aug 2020 Max-Heinrich Laves, Malte Tölle, Tobias Ortmaier

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.

Image Denoising Medical Image Denoising +1

Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer

no code implementations9 Jul 2020 Sontje Ihler, Max-Heinrich Laves, Tobias Ortmaier

Using flow estimations from teacher model FlowNet2 we specialize a fast student model FlowNet2S on the patient-specific domain.

Domain Adaptation Motion Estimation +2

Calibration of Model Uncertainty for Dropout Variational Inference

no code implementations20 Jun 2020 Max-Heinrich Laves, Sontje Ihler, Karl-Philipp Kortmann, Tobias Ortmaier

The model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration.

Bayesian Inference Variational Inference

Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior

no code implementations2 Aug 2019 Max-Heinrich Laves, Sontje Ihler, Tobias Ortmaier

Our approach uses the idea of deep image priors to combine convolutional networks with conventional registration methods based on manually engineered priors.

Deformable Medical Image Registration Image Registration +1

Uncertainty Quantification in Computer-Aided Diagnosis: Make Your Model say "I don't know" for Ambiguous Cases

1 code implementation2 Aug 2019 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.

Uncertainty Quantification

Semantic denoising autoencoders for retinal optical coherence tomography

no code implementations23 Mar 2019 Max-Heinrich Laves, Sontje Ihler, Lüder Alexander Kahrs, Tobias Ortmaier

Noise in speckle-prone optical coherence tomography tends to obfuscate important details necessary for medical diagnosis.

Denoising General Classification +1

Retinal OCT disease classification with variational autoencoder regularization

1 code implementation23 Mar 2019 Max-Heinrich Laves, Sontje Ihler, Lüder A. Kahrs, Tobias Ortmaier

A recent study established a diagnostic tool based on convolutional neural networks (CNN), which was trained on a large database of retinal OCT images.

Classification Clustering +2

Endoscopic vs. volumetric OCT imaging of mastoid bone structure for pose estimation in minimally invasive cochlear implant surgery

no code implementations19 Jan 2019 Max-Heinrich Laves, Sarah Latus, Jan Bergmeier, Tobias Ortmaier, Lüder A. Kahrs, Alexander Schlaefer

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.

Pose Estimation

A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation

1 code implementation16 Jul 2018 Max-Heinrich Laves, Jens Bicker, Lüder A. Kahrs, Tobias Ortmaier

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.

Data Augmentation Medical Image Segmentation +2

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