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Found 5 papers, 1 papers with code
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.
Repeatability of Multiparametric Prostate MRI Radiomics Features
In this study we assessed the repeatability of the values of radiomics features for small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI) images.
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?
