b'Enzo Ferrante'

research

∙ 02/07/2022

SUD: Supervision by Denoising for Medical Image Segmentation

Training a fully convolutional network for semantic segmentation typical...

Sean I. Young, et al. ∙

research

∙ 12/23/2021

Understanding the impact of class imbalance on the performance of chest x-ray image classifiers

This work aims to understand the impact of class imbalance on the perfor...

Candelaria Mosquera, et al. ∙

research

∙ 12/22/2021

Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation

Modern deep neural networks have achieved remarkable progress in medical...

Agostina Larrazabal, et al. ∙

research

∙ 08/03/2021

Domain Generalization via Gradient Surgery

In real-life applications, machine learning models often face scenarios ...

Lucas Mansilla, et al. ∙

research

∙ 06/17/2021

Hybrid graph convolutional neural networks for landmark-based anatomical segmentation

In this work we address the problem of landmark-based segmentation for a...

Nicolás Gaggion, et al. ∙

research

∙ 05/22/2021

Orthogonal Ensemble Networks for Biomedical Image Segmentation

Despite the astonishing performance of deep-learning based approaches fo...

Agostina J. Larrazabal, et al. ∙

research

∙ 09/29/2020

Cranial Implant Design via Virtual Craniectomy with Shape Priors

Cranial implant design is a challenging task, whose accuracy is crucial ...

Franco Matzkin, et al. ∙

research

∙ 09/10/2020

Unsupervised Domain Adaptation via CycleGAN for White Matter Hyperintensity Segmentation in Multicenter MR Images

Automatic segmentation of white matter hyperintensities in magnetic reso...

Julian Alberto Palladino, et al. ∙

research

∙ 07/07/2020

Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy

Decompressive craniectomy (DC) is a common surgical procedure consisting...

Franco Matzkin, et al. ∙

research

∙ 06/24/2020

Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders

We introduce Post-DAE, a post-processing method based on denoising autoe...

Agostina J. Larrazabal, et al. ∙

research

∙ 01/20/2020

Learning Deformable Registration of Medical Images with Anatomical Constraints

Deformable image registration is a fundamental problem in the field of m...

Lucas Mansilla, et al. ∙

research

∙ 06/05/2019

Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders

Deep convolutional neural networks (CNN) proved to be highly accurate to...

Agostina J. Larrazabal, et al. ∙

research

∙ 03/08/2019

Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets

Brain lesion and anatomy segmentation in magnetic resonance images are f...

Nicolas Roulet, et al. ∙

research

∙ 09/24/2018

Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration

Deformable registration has been one of the pillars of biomedical image ...

Enzo Ferrante, et al. ∙

research

∙ 08/23/2018

Left ventricle quantification through spatio-temporal CNNs

Cardiovascular diseases are among the leading causes of death globally. ...

Alejandro Debus, et al. ∙

research

∙ 06/05/2018

Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease

Graphs are widely used as a natural framework that captures interactions...

Sarah Parisot, et al. ∙

research

∙ 11/04/2017

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Deep learning approaches such as convolutional neural nets have consiste...

Konstantinos Kamnitsas, et al. ∙

research

∙ 07/19/2017

Deformable Registration through Learning of Context-Specific Metric Aggregation

We propose a novel weakly supervised discriminative algorithm for learni...

Enzo Ferrante, et al. ∙

research

∙ 05/22/2017

Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

Incorporation of prior knowledge about organ shape and location is key t...

Ozan Oktay, et al. ∙

research

∙ 03/08/2017

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease...

Sarah Parisot, et al. ∙

research

∙ 03/07/2017

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several c...

Ira Ktena, et al. ∙

research

∙ 02/06/2017

Slice-to-volume medical image registration: a survey

During the last decades, the research community of medical imaging has w...

Enzo Ferrante, et al. ∙

research

∙ 08/19/2016

Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields

Rigid slice-to-volume registration is a challenging task, which finds ap...

Roque Porchetto, et al. ∙

research

∙ 02/05/2016

Sub-cortical brain structure segmentation using F-CNN's

In this paper we propose a deep learning approach for segmenting sub-cor...

Mahsa Shakeri, et al. ∙

Enzo Ferrante

Featured Co-authors

Daniel Rueckert

Ben Glocker

Jose Dolz

Adrian V. Dalca

Bernhard Kainz

Polina Golland

Iasonas Kokkinos

Wenjia Bai

Konstantinos Kamnitsas

Nikos Paragios

Puneet K. Dokania

SUD: Supervision by Denoising for Medical Image Segmentation

Understanding the impact of class imbalance on the performance of chest x-ray image classifiers

Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation

Domain Generalization via Gradient Surgery

Hybrid graph convolutional neural networks for landmark-based anatomical segmentation

Orthogonal Ensemble Networks for Biomedical Image Segmentation

Cranial Implant Design via Virtual Craniectomy with Shape Priors

Unsupervised Domain Adaptation via CycleGAN for White Matter Hyperintensity Segmentation in Multicenter MR Images

Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy

Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders

Learning Deformable Registration of Medical Images with Anatomical Constraints

Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders

Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets

Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration

Left ventricle quantification through spatio-temporal CNNs

Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Deformable Registration through Learning of Context-Specific Metric Aggregation

Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

Spectral Graph Convolutions for Population-based Disease Prediction

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Slice-to-volume medical image registration: a survey

Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields

Sub-cortical brain structure segmentation using F-CNN's