309 citations · 729 across the 11 of their papers we have counts for
11 papers
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
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…
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures
Adrià Casamitjana, Santi Puch, Asier Aduriz +1
This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation in MR images. We address the problem using three different architectures that combine f…
Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling
Santi Puch, Asier Aduriz, Adrià Casamitjana +8
This paper describes a new neuroimaging analysis toolbox that allows for the modeling of nonlinear effects at the voxel level, overcoming limitations of methods based on linear mod…
Saliency maps on image hierarchies
Verónica Vilaplana
In this paper we propose two saliency models for salient object segmentation based on a hierarchical image segmentation, a tree-like structure that represents regions at different…
Improving Spatial Codification in Semantic Segmentation
Carles Ventura, Xavier Giró-i-Nieto, Verónica Vilaplana +3
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem. We propose to partition…