41 citations · 113 across the 21 of their papers we have counts for
8 papers · 1 filter
RadEdit: stress-testing biomedical vision models via diffusion image editing
Fernando Pérez-García, Sam Bond-Taylor, Pedro P. Sanchez +11
Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. Thi…
Group Distributionally Robust Knowledge Distillation
Konstantinos Vilouras, Xiao Liu, Pedro Sanchez +2
Knowledge distillation enables fast and effective transfer of features learned from a bigger model to a smaller one. However, distillation objectives are susceptible to sub-populat…
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…
A Causal Ordering Prior for Unsupervised Representation Learning
Avinash Kori, Pedro Sanchez, Konstantinos Vilouras +2
Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables. Causal representation learning (CRL), however, arg…
Compositionally Equivariant Representation Learning
Xiao Liu, Pedro Sanchez, Spyridon Thermos +2
Deep learning models often need sufficient supervision (i.e. labelled data) in order to be trained effectively. By contrast, humans can swiftly learn to identify important anatomy…
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…
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity
Raman Dutt, Linus Ericsson, Pedro Sanchez +2
Foundation models have significantly advanced medical image analysis through the pre-train fine-tune paradigm. Among various fine-tuning algorithms, Parameter-Efficient Fine-Tuning…
The role of noise in denoising models for anomaly detection in medical images
Antanas Kascenas, Pedro Sanchez, Patrick Schrempf +9
Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are dif…