M. Jorge Cardoso

research

∙ 08/23/2023

InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model

High-resolution (HR) MRI scans obtained from research-grade medical cent...

0 Jueqi Wang, et al. ∙

research

∙ 07/27/2023

Generative AI for Medical Imaging: extending the MONAI Framework

Recent advances in generative AI have brought incredible breakthroughs i...

0 Walter H. L Pinaya, et al. ∙

research

∙ 07/07/2023

Unsupervised 3D out-of-distribution detection with latent diffusion models

Methods for out-of-distribution (OOD) detection that scale to 3D data ar...

0 Mark S. Graham, et al. ∙

research

∙ 04/14/2023

Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection

Cancer is a highly heterogeneous condition that can occur almost anywher...

0 Ashay Patel, et al. ∙

research

∙ 12/29/2022

Current State of Community-Driven Radiological AI Deployment in Medical Imaging

Artificial Intelligence (AI) has become commonplace to solve routine eve...

24 Vikash Gupta, et al. ∙

research

∙ 12/08/2022

Transformer-based normative modelling for anomaly detection of early schizophrenia

Despite the impact of psychiatric disorders on clinical health, early-st...

0 Pedro F. da Costa, et al. ∙

research

∙ 11/14/2022

Denoising Diffusion Models for Out-of-Distribution Detection

Out-of-distribution detection is crucial to the safe deployment of machi...

0 Mark S. Graham, et al. ∙

research

∙ 11/04/2022

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most a...

6 M. Jorge Cardoso, et al. ∙

research

∙ 09/17/2022

Can segmentation models be trained with fully synthetically generated data?

In order to achieve good performance and generalisability, medical image...

54 Virginia Fernandez, et al. ∙

research

∙ 09/15/2022

Brain Imaging Generation with Latent Diffusion Models

Deep neural networks have brought remarkable breakthroughs in medical im...

13 Walter H. L Pinaya, et al. ∙

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....

0 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...

0 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...

13 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...

30 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...

31 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...

0 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...

11 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...

0 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...

0 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...

13 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...

11 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...

6 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 ...

57 Mariana da Silva, et al. ∙

research

∙ 10/05/2020

Test-time Unsupervised Domain Adaptation

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

0 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...

11 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...

42 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 ...

28 Mostafa Mehdipour Ghazi, et al. ∙

research

∙ 10/29/2019

Towards Quantifying Neurovascular Resilience

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

0 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...

18 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...

14 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 ...

12 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...

0 Kerstin Kläser, et al. ∙

research

∙ 08/14/2019

Robust parametric modeling of Alzheimer's disease progression

Quantitative characterization of disease progression using longitudinal ...

0 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...

1 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...

0 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...

10 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...

0 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...

6 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...

2 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 ...

2 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...

0 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...

0 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...

0 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, ...

0 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...

2 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...

6 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...

0 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...

0 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...

0 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...

0 Wenqi Li, et al. ∙

M. Jorge Cardoso

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