9 citations · 20 across the 11 of their papers we have counts for
11 papers
Latent-to-Latent Flow for Volumetric Stochastic Segmentation
Omar Todd, Sooha Kim, Raghav Mehta +5
Uncertainty arising from inter-observer variability in medical image segmentation plays an important role in developing treatment plans. Research in this area is inhibited by the l…
Subgroup performance analysis of adaptation strategies for chest X-ray foundation models
Dhruv Gupta, Emma A. M. Stanley, Fabio De Sousa Ribeiro +2
Foundation models are increasingly adapted for downstream medical imaging tasks, yet the influence of the chosen adaptation strategy on subgroup fairness remains poorly understood.…
Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers
Fabio De Sousa Ribeiro, Emma A. M. Stanley, Charles Jones +7
We introduce the first generative foundation model for chest radiograph synthesis trained from scratch at the billion-parameter scale. Existing radiographic AI models often suffer…
Counterfactual Stress Testing for Image Classification Models
Moritz Stammel, Fabio De Sousa Ribeiro, Raghav Mehta +2
Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in demographics, scanner hardware, or acquisition protocols…
Factored Classifier-Free Guidance
Tian Xia, Fabio De Sousa Ribeiro, Rajat R Rasal +3
Counterfactual generation aims to simulate realistic hypothetical outcomes under causal interventions. Diffusion models have emerged as a powerful tool for this task, combining DDI…
Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention
Avinash Kori, Francesco Locatello, Ainkaran Santhirasekaram +3
Learning modular object-centric representations is crucial for systematic generalization. Existing methods show promising object-binding capabilities empirically, but theoretical i…
Mitigating attribute amplification in counterfactual image generation
Tian Xia, Mélanie Roschewitz, Fabio De Sousa Ribeiro +2
Causal generative modelling is gaining interest in medical imaging due to its ability to answer interventional and counterfactual queries. Most work focuses on generating counterfa…
Counterfactual contrastive learning: robust representations via causal image synthesis
Melanie Roschewitz, Fabio De Sousa Ribeiro, Tian Xia +2
Contrastive pretraining is well-known to improve downstream task performance and model generalisation, especially in limited label settings. However, it is sensitive to the choice…
Demystifying Variational Diffusion Models
Fabio De Sousa Ribeiro, Ben Glocker
Despite the growing interest in diffusion models, gaining a deep understanding of the model class remains an elusive endeavour, particularly for the uninitiated in non-equilibrium…
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…
Learning with Capsules: A Survey
Fabio De Sousa Ribeiro, Kevin Duarte, Miles Everett +2
Capsule networks were proposed as an alternative approach to Convolutional Neural Networks (CNNs) for learning object-centric representations, which can be leveraged for improved g…