5 citations · 6 across the 4 of their papers we have counts for
5 papers
Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation
Franco Matzkin, Agostina Larrazabal, Diego H Milone +2
Accurate segmentation of white matter hyperintensities (WMH) is crucial for clinical decision-making, particularly in the context of multiple sclerosis. However, domain shifts, suc…
Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation
Agostina Larrazabal, Cesar Martinez, Jose Dolz +1
Modern deep neural networks achieved remarkable progress in medical image segmentation tasks. However, it has recently been observed that they tend to produce overconfident estimat…
Orthogonal Ensemble Networks for Biomedical Image Segmentation
Agostina J. Larrazabal, César Martínez, Jose Dolz +1
Despite the astonishing performance of deep-learning based approaches for visual tasks such as semantic segmentation, they are known to produce miscalibrated predictions, which cou…
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders
Agostina J Larrazabal, César Martínez, Ben Glocker +1
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms.…
Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders
Agostina J. Larrazabal, Cesar Martinez, Enzo Ferrante
Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for…