24 citations · 66 across the 14 of their papers we have counts for
16 papers
Posterior temperature optimized Bayesian models for inverse problems in medical imaging
Max-Heinrich Laves, Malte Tölle, Alexander Schlaefer +1
We present Posterior Temperature Optimized Bayesian Inverse Models (POTOBIM), an unsupervised Bayesian approach to inverse problems in medical imaging using mean-field variational…
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction
Marcel Bengs, Finn Behrendt, Max-Heinrich Laves +3
Lesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consum…
Robotic Tissue Sampling for Safe Post-mortem Biopsy in Infectious Corpses
Maximilian Neidhardt, Stefan Gerlach, Robin Mieling +12
In pathology and legal medicine, the histopathological and microbiological analysis of tissue samples from infected deceased is a valuable information for developing treatment stra…
Posterior Temperature Optimization in Variational Inference for Inverse Problems
Max-Heinrich Laves, Malte Tölle, Alexander Schlaefer +1
Bayesian methods feature useful properties for solving inverse problems, such as tomographic reconstruction. The prior distribution introduces regularization, which helps solving t…
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…
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
Max-Heinrich Laves, Malte Tölle, Tobias Ortmaier
Uncertainty quantification in inverse medical imaging tasks with deep learning has received little attention. However, deep models trained on large data sets tend to hallucinate an…
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…
Retinal OCT disease classification with variational autoencoder regularization
Max-Heinrich Laves, Sontje Ihler, Lüder A. Kahrs +1
According to the World Health Organization, 285 million people worldwide live with visual impairment. The most commonly used imaging technique for diagnosis in ophthalmology is opt…
Semantic denoising autoencoders for retinal optical coherence tomography
Max-Heinrich Laves, Sontje Ihler, Lüder Alexander Kahrs +1
Noise in speckle-prone optical coherence tomography tends to obfuscate important details necessary for medical diagnosis. In this paper, a denoising approach that preserves disease…
Endoscopic vs. volumetric OCT imaging of mastoid bone structure for pose estimation in minimally invasive cochlear implant surgery
Max-Heinrich Laves, Sarah Latus, Jan Bergmeier +3
Purpose: The facial recess is a delicate structure that must be protected in minimally invasive cochlear implant surgery. Current research estimates the drill trajectory by using e…
Deep learning based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography
Max-Heinrich Laves, Lüder A. Kahrs, Tobias Ortmaier
In microsurgery, lasers have emerged as precise tools for bone ablation. A challenge is automatic control of laser bone ablation with 4D optical coherence tomography (OCT). OCT as…
A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation
Max-Heinrich Laves, Jens Bicker, Lüder A. Kahrs +1
Purpose Automated segmentation of anatomical structures in medical image analysis is a prerequisite for autonomous diagnosis as well as various computer and robot aided interventio…