M. Jorge Cardoso

research

∙ 06/13/2022

Fitting Segmentation Networks on Varying Image Resolutions using Splatting

Data used in image segmentation are not always defined on the same grid....

Mikael Brudfors, et al. ∙

research

∙ 06/09/2022

Solid NURBS Conforming Scaffolding for Isogeometric Analysis

This work introduces a scaffolding framework to compactly parametrise so...

Stefano Moriconi, et al. ∙

research

∙ 06/07/2022

Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models

Deep generative models have emerged as promising tools for detecting arb...

Walter H. L Pinaya, et al. ∙

research

∙ 05/21/2022

Transformer-based out-of-distribution detection for clinically safe segmentation

In a clinical setting it is essential that deployed image processing sys...

Mark S. Graham, et al. ∙

research

∙ 03/23/2022

MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images

The lack of annotated datasets is a major challenge in training new task...

Andres Diaz-Pinto, et al. ∙

research

∙ 02/23/2022

Augmentation based unsupervised domain adaptation

The insertion of deep learning in medical image analysis had lead to the...

Mauricio Orbes-Arteaga, et al. ∙

research

∙ 11/07/2021

Acquisition-invariant brain MRI segmentation with informative uncertainties

Combining multi-site data can strengthen and uncover trends, but is a ta...

Pedro Borges, et al. ∙

research

∙ 11/04/2021

The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties

Being able to adequately process and combine data arising from different...

Pedro Borges, et al. ∙

research

∙ 10/17/2021

Deep forecasting of translational impact in medical research

The value of biomedical research–a 1.7 trillion annual investment–is ult...

Amy PK Nelson, et al. ∙

research

∙ 09/06/2021

A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation

Quality control (QC) of MR images is essential to ensure that downstream...

Richard Shaw, et al. ∙

research

∙ 08/18/2021

Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks

Biomechanical modeling of tissue deformation can be used to simulate dif...

Mariana da Silva, et al. ∙

research

∙ 04/12/2021

Common Limitations of Image Processing Metrics: A Picture Story

While the importance of automatic image analysis is increasing at an eno...

Annika Reinke, et al. ∙

research

∙ 12/14/2020

Biomechanical modelling of brain atrophy through deep learning

We present a proof-of-concept, deep learning (DL) based, differentiable ...

Mariana da Silva, et al. ∙

research

∙ 10/05/2020

Test-time Unsupervised Domain Adaptation

Convolutional neural networks trained on publicly available medical imag...

Thomas Varsavsky, et al. ∙

research

∙ 02/13/2020

Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

The increasing efficiency and compactness of deep learning architectures...

Petru-Daniel Tudosiu, et al. ∙

research

∙ 01/31/2020

A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality

Quality control (QC) of medical images is essential to ensure that downs...

Richard Shaw, et al. ∙

research

∙ 12/22/2019

On the Initialization of Long Short-Term Memory Networks

Weight initialization is important for faster convergence and stability ...

Mostafa Mehdipour Ghazi, et al. ∙

research

∙ 10/29/2019

Towards Quantifying Neurovascular Resilience

Whilst grading neurovascular abnormalities is critical for prompt surgic...

Stefano Moriconi, et al. ∙

research

∙ 10/02/2019

Privacy-preserving Federated Brain Tumour Segmentation

Due to medical data privacy regulations, it is often infeasible to colle...

Wenqi Li, et al. ∙

research

∙ 09/04/2019

Let's agree to disagree: learning highly debatable multirater labelling

Classification and differentiation of small pathological objects may gre...

Carole H Sudre, et al. ∙

research

∙ 08/26/2019

Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels

The performance of multi-task learning in Convolutional Neural Networks ...

Felix J. S. Bragman, et al. ∙

research

∙ 08/21/2019

Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning

The ability to synthesise Computed Tomography images - commonly known as...

Kerstin Kläser, et al. ∙

research

∙ 08/14/2019

Robust parametric modeling of Alzheimer's disease progression

Quantitative characterization of disease progression using longitudinal ...

Mostafa Mehdipour Ghazi, et al. ∙

research

∙ 07/25/2019

As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging

Counting is a fundamental task in biomedical imaging and count is an imp...

Zach Eaton-Rosen, et al. ∙

research

∙ 04/01/2019

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Quantification of cerebral white matter hyperintensities (WMH) of presum...

Hugo J. Kuijf, et al. ∙

research

∙ 03/17/2019

Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

Disease progression modeling (DPM) using longitudinal data is a challeng...

Mostafa Mehdipour Ghazi, et al. ∙

research

∙ 02/25/2019

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Semantic segmentation of medical images aims to associate a pixel with a...

Amber L. Simpson, et al. ∙

research

∙ 12/21/2018

3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects

Extremely small objects (ESO) have become observable on clinical routine...

Carole H Sudre, et al. ∙

research

∙ 09/14/2018

Elastic Registration of Geodesic Vascular Graphs

Vascular graphs can embed a number of high-level features, from morpholo...

Stefano Moriconi, et al. ∙

research

∙ 08/22/2018

Deep Boosted Regression for MR to CT Synthesis

Attenuation correction is an essential requirement of positron emission ...

Kerstin Kläser, et al. ∙

research

∙ 08/20/2018

Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

Segmenting vascular pathologies such as white matter lesions in Brain ma...

Mauricio Orbes-Arteaga, et al. ∙

research

∙ 08/16/2018

Robust training of recurrent neural networks to handle missing data for disease progression modeling

Disease progression modeling (DPM) using longitudinal data is a challeng...

Mostafa Mehdipour Ghazi, et al. ∙

research

∙ 07/17/2018

PIMMS: Permutation Invariant Multi-Modal Segmentation

In a research context, image acquisition will often involve a pre-define...

Thomas Varsavsky, et al. ∙

research

∙ 06/22/2018

Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions

Automated medical image segmentation, specifically using deep learning, ...

Zach Eaton-Rosen, et al. ∙

research

∙ 06/18/2018

Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning

Multi-task neural network architectures provide a mechanism that jointly...

Felix J. S. Bragman, et al. ∙

research

∙ 06/08/2018

VTrails: Inferring Vessels with Geodesic Connectivity Trees

The analysis of vessel morphology and connectivity has an impact on a nu...

Stefano Moriconi, et al. ∙

research

∙ 09/11/2017

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are i...

Eli Gibson, et al. ∙

research

∙ 09/08/2017

An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

Deep convolutional neural networks (CNNs) have shown excellent performan...

Lorenz Berger, et al. ∙

research

∙ 07/11/2017

Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

Deep-learning has proved in recent years to be a powerful tool for image...

Carole H Sudre, et al. ∙

research

∙ 07/06/2017

On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

Deep convolutional neural networks are powerful tools for learning visua...

Wenqi Li, et al. ∙

research

∙ 07/05/2017

Generative diffeomorphic atlas construction from brain and spinal cord MRI data

In this paper we will focus on the potential and on the challenges assoc...

Claudia Blaiotta, et al. ∙

M. Jorge Cardoso

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