309 citations · 370 across the 6 of their papers we have counts for
7 papers
MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures
Laura Mora Ballestar, Veronica Vilaplana
Automation of brain tumor segmentation in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural ne…
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation
Laura Mora Ballestar, Veronica Vilaplana
Automation of brain tumors in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs…
Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning
Ignasi Mas, Josep Ramon Morros, Veronica Vilaplana
Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of s…
BCN20000: Dermoscopic Lesions in the Wild
Marc Combalia, Noel C. F. Codella, Veronica Rotemberg +8
This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Clínic in Barcelona.…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…
Brain MRI super-resolution using 3D generative adversarial networks
Irina Sanchez, Veronica Vilaplana
In this work we propose an adversarial learning approach to generate high resolution MRI scans from low resolution images. The architecture, based on the SRGAN model, adopts 3D con…