Search Results for author:
Found 11 papers, 4 papers with code
Automatic classification of prostate MR series type using image content and metadata
With the wealth of medical image data, efficient curation is essential.
Learning Expected Appearances for Intraoperative Registration during Neurosurgery
In contrast to conventional methods, our approach transfers the processing tasks to the preoperative stage, reducing thereby the impact of low-resolution, distorted, and noisy intraoperative images, that often degrade the registration accuracy.
Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations
We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities.
Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound
B-line artifacts in LUS videos are key findings associated with pulmonary congestion.
Peak learning of mass spectrometry imaging data using artificial neural networks
Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis.
PEP: Parameter Ensembling by Perturbation
In most experiments, PEP provides a small improvement in performance, and, in some cases, a substantial improvement in empirical calibration.
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
We make the following contributions: 1) We systematically compare cross entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2) We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples.
Deep Information Theoretic Registration
This paper establishes an information theoretic framework for deep metric based image registration techniques.
Semi-Supervised Deep Metrics for Image Registration
In this paper, we propose a strategy for learning such metrics from roughly aligned training data.
Model-based Catheter Segmentation in MRI-images
Accurate and reliable segmentation of catheters in MR-guided interventions remains a challenge, and a step of critical importance in clinical workflows.
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?
