44 citations · 128 across the 10 of their papers we have counts for
11 papers
A 3D generative model of pathological multi-modal MR images and segmentations
Virginia Fernandez, Walter Hugo Lopez Pinaya, Pedro Borges +3
Generative modelling and synthetic data can be a surrogate for real medical imaging datasets, whose scarcity and difficulty to share can be a nuisance when delivering accurate deep…
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…
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…
Can segmentation models be trained with fully synthetically generated data?
Virginia Fernandez, Walter Hugo Lopez Pinaya, Pedro Borges +4
In order to achieve good performance and generalisability, medical image segmentation models should be trained on sizeable datasets with sufficient variability. Due to ethics and g…
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…