77 citations · 133 across the 15 of their papers we have counts for
16 papers
Trajectory Replay: Continuous-Time Mixture Transport for Lifelong Generative Models
Alessandro Palma, Charles Jones, Tristan Cinquin +2
Adapting pre-trained continuous-time generative models to sequentially incoming data without forgetting earlier tasks is a central challenge in lifelong learning. A leading strateg…
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…
Elucidating the Design Space of Flow Matching for Cellular Microscopy
Charles Jones, Emmanuel Noutahi, Jason Hartford +1
Flow-matching generative models are increasingly used to simulate cell responses to biological perturbations. However, the design space for building such models is large and undere…
When does Subgroup Balance Matter? Predicting Performance Sensitivity from Latent Separability
Anissa Alloula, Charles Jones, Zuzanna Wakefield-Skorniewska +2
Unequal representation of demographic groups in training data poses challenges to model generalisation across populations. Standard practice assumes that balancing subgroup represe…
A Primer on Causal and Statistical Dataset Biases for Fair and Robust Image Analysis
Charles Jones, Ben Glocker
Machine learning methods often fail when deployed in the real world. Worse still, they fail in high-stakes situations and across socially sensitive lines. These issues have a chill…
Flow Stochastic Segmentation Networks
Fabio De Sousa Ribeiro, Omar Todd, Charles Jones +3
We introduce the Flow Stochastic Segmentation Network (Flow-SSN), a generative segmentation model family featuring discrete-time autoregressive and modern continuous-time flow vari…
Subgroups Matter for Robust Bias Mitigation
Anissa Alloula, Charles Jones, Ben Glocker +1
Despite the constant development of new bias mitigation methods for machine learning, no method consistently succeeds, and a fundamental question remains unanswered: when and why d…
Automatic dataset shift identification to support safe deployment of medical imaging AI
Mélanie Roschewitz, Raghav Mehta, Charles Jones +1
Shifts in data distribution can substantially harm the performance of clinical AI models and lead to misdiagnosis. Hence, various methods have been developed to detect the presence…
Rethinking Fair Representation Learning for Performance-Sensitive Tasks
Charles Jones, Fabio de Sousa Ribeiro, Mélanie Roschewitz +2
We investigate the prominent class of fair representation learning methods for bias mitigation. Using causal reasoning to define and formalise different sources of dataset bias, we…
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…
No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging
Charles Jones, Daniel C. Castro, Fabio De Sousa Ribeiro +3
As machine learning methods gain prominence within clinical decision-making, addressing fairness concerns becomes increasingly urgent. Despite considerable work dedicated to detect…
The Role of Subgroup Separability in Group-Fair Medical Image Classification
Charles Jones, Mélanie Roschewitz, Ben Glocker
We investigate performance disparities in deep classifiers. We find that the ability of classifiers to separate individuals into subgroups varies substantially across medical imagi…
Risk of Bias in Chest Radiography Deep Learning Foundation Models
Ben Glocker, Charles Jones, Melanie Roschewitz +1
Purpose: To analyze a recently published chest radiography foundation model for the presence of biases that could lead to subgroup performance disparities across biological sex and…
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…
Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms
Mélanie Bernhardt, Charles Jones, Ben Glocker
An increasing number of reports raise concerns about the risk that machine learning algorithms could amplify health disparities due to biases embedded in the training data. Seyyed-…
Algorithmic encoding of protected characteristics in image-based models for disease detection
Ben Glocker, Charles Jones, Melanie Bernhardt +1
It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use…