3.1k citations · 5.7k across the 38 of their papers we have counts for
40 papers
Resolution Invariant Autoencoder
Ashay Patel, Michela Antonelli, Sebastien Ourselin +1
Deep learning has significantly advanced medical imaging analysis, yet variations in image resolution remain an overlooked challenge. Most methods address this by resampling images…
Framework to generate perfusion map from CT and CTA images in patients with acute ischemic stroke: A longitudinal and cross-sectional study
Chayanin Tangwiriyasakul, Pedro Borges, Stefano Moriconi +6
Stroke is a leading cause of disability and death. Effective treatment decisions require early and informative vascular imaging. 4D perfusion imaging is ideal but rarely available…
RAISE -- Radiology AI Safety, an End-to-end lifecycle approach
M. Jorge Cardoso, Julia Moosbauer, Tessa S. Cook +11
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks a…
Generative AI for Medical Imaging: extending the MONAI Framework
Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot +21
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to he…
Unsupervised 3D out-of-distribution detection with latent diffusion models
Mark S. Graham, Walter Hugo Lopez Pinaya, Paul Wright +8
Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilis…
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images
Andres Diaz-Pinto, Pritesh Mehta, Sachidanand Alle +17
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be te…
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection
Ashay Patel, Petru-Danial Tudiosu, Walter H. L. Pinaya +4
Cancer is a highly heterogeneous condition that can occur almost anywhere in the human body. 18F-fluorodeoxyglucose is an imaging modality commonly used to detect cancer due to its…
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Vikash Gupta, Barbaros Selnur Erdal, Carolina Ramirez +24
Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload o…
MONAI: An open-source framework for deep learning in healthcare
M. Jorge Cardoso, Wenqi Li, Richard Brown +53
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagn…
Denoising diffusion models for out-of-distribution detection
Mark S. Graham, Walter H. L. Pinaya, Petru-Daniel Tudosiu +3
Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based…
Brain Imaging Generation with Latent Diffusion Models
Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon +5
Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects…
Morphology-preserving Autoregressive 3D Generative Modelling of the Brain
Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Mark S. Graham +10
Human anatomy, morphology, and associated diseases can be studied using medical imaging data. However, access to medical imaging data is restricted by governance and privacy concer…
Fitting Segmentation Networks on Varying Image Resolutions using Splatting
Mikael Brudfors, Yael Balbastre, John Ashburner +4
Data used in image segmentation are not always defined on the same grid. This is particularly true for medical images, where the resolution, field-of-view and orientation can diffe…
Solid NURBS Conforming Scaffolding for Isogeometric Analysis
Stefano Moriconi, Parashkev Nachev, Sebastien Ourselin +1
This work introduces a scaffolding framework to compactly parametrise solid structures with conforming NURBS elements for isogeometric analysis. A novel formulation introduces a to…
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
Walter H. L. Pinaya, Mark S. Graham, Robert Gray +12
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive trans…
Transformer-based out-of-distribution detection for clinically safe segmentation
Mark S Graham, Petru-Daniel Tudosiu, Paul Wright +9
In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently…
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
Andres Diaz-Pinto, Sachidanand Alle, Vishwesh Nath +14
The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation is extremely expensive an…
Acquisition-invariant brain MRI segmentation with informative uncertainties
Pedro Borges, Richard Shaw, Thomas Varsavsky +5
Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any down…
The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties
Pedro Borges, Richard Shaw, Thomas Varsavsky +5
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter depen…
Deep forecasting of translational impact in medical research
Amy PK Nelson, Robert J Gray, James K Ruffle +10
The value of biomedical research--a $1.7 trillion annual investment--is ultimately determined by its downstream, real-world impact. Current objective predictors of impact rest on p…
A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation
Richard Shaw, Carole H. Sudre, Sebastien Ourselin +2
Quality control (QC) of MR images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visua…
Estimating MRI Image Quality via Image Reconstruction Uncertainty
Richard Shaw, Carole H. Sudre, Sebastien Ourselin +1
Quality control (QC) in medical image analysis is time-consuming and laborious, leading to increased interest in automated methods. However, what is deemed suitable quality for alg…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
An MRF-UNet Product of Experts for Image Segmentation
Mikael Brudfors, Yaël Balbastre, John Ashburner +4
While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribu…