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...
share
research
∙
07/27/2023
Generative AI for Medical Imaging: extending the MONAI Framework
Recent advances in generative AI have brought incredible breakthroughs i...
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...
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...
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...
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...
research
∙
11/14/2022
Denoising Diffusion Models for Out-of-Distribution Detection
Out-of-distribution detection is crucial to the safe deployment of machi...
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...
research
∙
09/17/2022
Can segmentation models be trained with fully synthetically generated data?
In order to achieve good performance and generalisability, medical image...
research
∙
09/15/2022
Brain Imaging Generation with Latent Diffusion Models
Deep neural networks have brought remarkable breakthroughs in medical im...
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....
research
∙
06/09/2022
Solid NURBS Conforming Scaffolding for Isogeometric Analysis
This work introduces a scaffolding framework to compactly parametrise so...
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...
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...
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...
research
∙
02/23/2022
Augmentation based unsupervised domain adaptation
The insertion of deep learning in medical image analysis had lead to the...
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...
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...
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...
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...
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...
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...
research
∙
12/14/2020
Biomechanical modelling of brain atrophy through deep learning
We present a proof-of-concept, deep learning (DL) based, differentiable ...
research
∙
10/05/2020
Test-time Unsupervised Domain Adaptation
Convolutional neural networks trained on publicly available medical imag...
research
∙
02/13/2020
Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE
The increasing efficiency and compactness of deep learning architectures...
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...
research
∙
12/22/2019
On the Initialization of Long Short-Term Memory Networks
Weight initialization is important for faster convergence and stability ...
research
∙
10/29/2019
Towards Quantifying Neurovascular Resilience
Whilst grading neurovascular abnormalities is critical for prompt surgic...
research
∙
10/02/2019
Privacy-preserving Federated Brain Tumour Segmentation
Due to medical data privacy regulations, it is often infeasible to colle...
research
∙
09/04/2019
Let's agree to disagree: learning highly debatable multirater labelling
Classification and differentiation of small pathological objects may gre...
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 ...
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...
research
∙
08/14/2019
Robust parametric modeling of Alzheimer's disease progression
Quantitative characterization of disease progression using longitudinal ...
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...
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...
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...
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...
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...
research
∙
09/14/2018
Elastic Registration of Geodesic Vascular Graphs
Vascular graphs can embed a number of high-level features, from morpholo...
research
∙
08/22/2018
Deep Boosted Regression for MR to CT Synthesis
Attenuation correction is an essential requirement of positron emission ...
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...
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...
research
∙
07/17/2018
PIMMS: Permutation Invariant Multi-Modal Segmentation
In a research context, image acquisition will often involve a pre-define...
research
∙
06/22/2018
Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
Automated medical image segmentation, specifically using deep learning, ...
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...
research
∙
06/08/2018
VTrails: Inferring Vessels with Geodesic Connectivity Trees
The analysis of vessel morphology and connectivity has an impact on a nu...
research
∙
09/11/2017
NiftyNet: a deep-learning platform for medical imaging
Medical image analysis and computer-assisted intervention problems are i...
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...
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...
research
∙
07/06/2017