8 papers
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…
Representation Invariance and Allocation: When Subgroup Balance Matters
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…