Search Results for author:
Found 7 papers, 2 papers with code
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.
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.
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Segmentation methods had to be containerized and submitted to the challenge organizers.
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.
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?
