44 citations · 126 across the 9 of their papers we have counts for
9 papers
Diffusion-Based Quality Control of Medical Image Segmentations across Organs
Vincenzo Marcianò, Hava Chaptoukaev, Virginia Fernandez +4
Medical image segmentation using deep learning (DL) has enabled the development of automated analysis pipelines for large-scale population studies. However, state-of-the-art DL met…
A methodology for clinically driven interactive segmentation evaluation
Parhom Esmaeili, Virginia Fernandez, Pedro Borges +3
Interactive segmentation is a promising strategy for building robust, generalisable algorithms for volumetric medical image segmentation. However, inconsistent and clinically unrea…
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…
Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models
Virginia Fernandez, Pedro Sanchez, Walter Hugo Lopez Pinaya +3
Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself. We introduce Privacy Distillation, a framework that allow…
Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers
Manuel Lagunas, Brayan Impata, Victor Martinez +4
Fine-grained classification is a challenging task that involves identifying subtle differences between objects within the same category. This task is particularly challenging in sc…
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…
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…