41 citations · 113 across the 21 of their papers we have counts for
7 papers · 1 filter
Diffusion Models for Causal Discovery via Topological Ordering
Pedro Sanchez, Xiao Liu, Alison Q O'Neil +1
Discovering causal relations from observational data becomes possible with additional assumptions such as considering the functional relations to be constrained as nonlinear with a…
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information
Xiao Liu, Spyridon Thermos, Pedro Sanchez +2
Learning disentangled representations requires either supervision or the introduction of specific model designs and learning constraints as biases. InfoGAN is a popular disentangle…
What is Healthy? Generative Counterfactual Diffusion for Lesion Localization
Pedro Sanchez, Antanas Kascenas, Xiao Liu +2
Reducing the requirement for densely annotated masks in medical image segmentation is important due to cost constraints. In this paper, we consider the problem of inferring pixel-l…
vMFNet: Compositionality Meets Domain-generalised Segmentation
Xiao Liu, Spyridon Thermos, Pedro Sanchez +2
Training medical image segmentation models usually requires a large amount of labeled data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from m…
Causal Machine Learning for Healthcare and Precision Medicine
Pedro Sanchez, Jeremy P. Voisey, Tian Xia +3
Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a…
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification
Tian Xia, Pedro Sanchez, Chen Qin +1
Due to the limited availability of medical data, deep learning approaches for medical image analysis tend to generalise poorly to unseen data. Augmenting data during training with…
Diffusion Causal Models for Counterfactual Estimation
Pedro Sanchez, Sotirios A. Tsaftaris
We consider the task of counterfactual estimation from observational imaging data given a known causal structure. In particular, quantifying the causal effect of interventions for…