M. Jorge Cardoso

Biomechanical modelling of brain atrophy through deep learning

We present a proof-of-concept, deep learning (DL) based, differentiable ...
12/14/2020 ∙ by Mariana da Silva, et al. ∙ 57 ∙ share

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Test-time Unsupervised Domain Adaptation

Convolutional neural networks trained on publicly available medical imag...
10/05/2020 ∙ by Thomas Varsavsky, et al. ∙ 0 ∙ share

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Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

The increasing efficiency and compactness of deep learning architectures...
02/13/2020 ∙ by Petru-Daniel Tudosiu, et al. ∙ 11 ∙ share

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A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality

Quality control (QC) of medical images is essential to ensure that downs...
01/31/2020 ∙ by Richard Shaw, et al. ∙ 42 ∙ share

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On the Initialization of Long Short-Term Memory Networks

Weight initialization is important for faster convergence and stability ...
12/22/2019 ∙ by Mostafa Mehdipour Ghazi, et al. ∙ 28 ∙ share

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Towards Quantifying Neurovascular Resilience

Whilst grading neurovascular abnormalities is critical for prompt surgic...
10/29/2019 ∙ by Stefano Moriconi, et al. ∙ 0 ∙ share

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Privacy-preserving Federated Brain Tumour Segmentation

Due to medical data privacy regulations, it is often infeasible to colle...
10/02/2019 ∙ by Wenqi Li, et al. ∙ 18 ∙ share

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

Classification and differentiation of small pathological objects may gre...
09/04/2019 ∙ by Carole H Sudre, et al. ∙ 14 ∙ share

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

The performance of multi-task learning in Convolutional Neural Networks ...
08/26/2019 ∙ by Felix J. S. Bragman, et al. ∙ 12 ∙ share

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

The ability to synthesise Computed Tomography images - commonly known as...
08/21/2019 ∙ by Kerstin Kläser, et al. ∙ 0 ∙ share

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Robust parametric modeling of Alzheimer's disease progression

Quantitative characterization of disease progression using longitudinal ...
08/14/2019 ∙ by Mostafa Mehdipour Ghazi, et al. ∙ 0 ∙ share

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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...
07/25/2019 ∙ by Zach Eaton-Rosen, et al. ∙ 1 ∙ share

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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...
04/01/2019 ∙ by Hugo J. Kuijf, et al. ∙ 0 ∙ share

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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...
03/17/2019 ∙ by Mostafa Mehdipour Ghazi, et al. ∙ 10 ∙ share

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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...
02/25/2019 ∙ by Amber L. Simpson, et al. ∙ 0 ∙ share

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

Extremely small objects (ESO) have become observable on clinical routine...
12/21/2018 ∙ by Carole H Sudre, et al. ∙ 6 ∙ share

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Elastic Registration of Geodesic Vascular Graphs

Vascular graphs can embed a number of high-level features, from morpholo...
09/14/2018 ∙ by Stefano Moriconi, et al. ∙ 2 ∙ share

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Deep Boosted Regression for MR to CT Synthesis

Attenuation correction is an essential requirement of positron emission ...
08/22/2018 ∙ by Kerstin Kläser, et al. ∙ 2 ∙ share

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

Segmenting vascular pathologies such as white matter lesions in Brain ma...
08/20/2018 ∙ by Mauricio Orbes-Arteaga, et al. ∙ 0 ∙ share

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

Disease progression modeling (DPM) using longitudinal data is a challeng...
08/16/2018 ∙ by Mostafa Mehdipour Ghazi, et al. ∙ 0 ∙ share

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PIMMS: Permutation Invariant Multi-Modal Segmentation

In a research context, image acquisition will often involve a pre-define...
07/17/2018 ∙ by Thomas Varsavsky, et al. ∙ 0 ∙ share

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

Automated medical image segmentation, specifically using deep learning, ...
06/22/2018 ∙ by Zach Eaton-Rosen, et al. ∙ 0 ∙ share

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

Multi-task neural network architectures provide a mechanism that jointly...
06/18/2018 ∙ by Felix J. S. Bragman, et al. ∙ 2 ∙ share

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VTrails: Inferring Vessels with Geodesic Connectivity Trees

The analysis of vessel morphology and connectivity has an impact on a nu...
06/08/2018 ∙ by Stefano Moriconi, et al. ∙ 6 ∙ share

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NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are i...
09/11/2017 ∙ by Eli Gibson, et al. ∙ 0 ∙ share

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

Deep convolutional neural networks (CNNs) have shown excellent performan...
09/08/2017 ∙ by Lorenz Berger, et al. ∙ 0 ∙ share

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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...
07/11/2017 ∙ by Carole H Sudre, et al. ∙ 0 ∙ share

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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...
07/06/2017 ∙ by Wenqi Li, et al. ∙ 0 ∙ share

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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...
07/05/2017 ∙ by Claudia Blaiotta, et al. ∙ 0 ∙ share

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M. Jorge Cardoso

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