41 citations · 74 across the 7 of their papers we have counts for
8 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…