27 citations · 29 across the 7 of their papers we have counts for
5 papers · 1 filter
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.…
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
Giang Nguyen, Raghav Mehta, Emma A. M. Stanley +4
Foundation models are increasingly used as image feature extractors for mammography, but their robustness under external domain shift remains unclear. We benchmark 15 foundation-mo…
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…
Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classification
Emma A. M. Stanley, Raghav Mehta, Mélanie Roschewitz +2
Systematic mislabelling affecting specific subgroups (i.e., label bias) in medical imaging datasets represents an understudied issue concerning the fairness of medical AI systems.…
Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging
Emma A. M. Stanley, Raissa Souza, Anthony Winder +4
Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all so…