108 citations · 115 across the 6 of their papers we have counts for
6 papers
High Fidelity Image Counterfactuals with Probabilistic Causal Models
Fabio De Sousa Ribeiro, Tian Xia, Miguel Monteiro +2
We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models. Estimation of interven…
Measuring axiomatic soundness of counterfactual image models
Miguel Monteiro, Fabio De Sousa Ribeiro, Nick Pawlowski +2
We present a general framework for evaluating image counterfactuals. The power and flexibility of deep generative models make them valuable tools for learning mechanisms in structu…
Analysing the effectiveness of a generative model for semi-supervised medical image segmentation
Margherita Rosnati, Fabio De Sousa Ribeiro, Miguel Monteiro +2
Image segmentation is important in medical imaging, providing valuable, quantitative information for clinical decision-making in diagnosis, therapy, and intervention. The state-of-…
Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury
Margherita Rosnati, Eyal Soreq, Miguel Monteiro +8
The accurate prognosis for traumatic brain injury (TBI) patients is difficult yet essential to inform therapy, patient management, and long-term after-care. Patient characteristics…
Structured Uncertainty in the Observation Space of Variational Autoencoders
James Langley, Miguel Monteiro, Charles Jones +2
Variational autoencoders (VAEs) are a popular class of deep generative models with many variants and a wide range of applications. Improvements upon the standard VAE mostly focus o…
Active label cleaning for improved dataset quality under resource constraints
Melanie Bernhardt, Daniel C. Castro, Ryutaro Tanno +9
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment…