204 citations · 228 across the 24 of their papers we have counts for
26 papers
Estimating Performance for Safe Deployment of Machine Learning Models
Zeju Li, Konstantinos Kamnitsas, Mobarakol Islam +4
The main challenge in applying deep neural networks to real-world scenarios lies in distributional discrepancies between training and test datasets, often caused by variations in d…
Memorisation bias in medical AI
Moritz A. Knolle, Martin J. Menten, Laurin Lux +5
Medical AI models hold immense potential to improve patient outcomes, but they are also known to unintentionally memorise individual records from their training datasets. While suc…
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…
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…
Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesis
Tian Xia, Matthew Sinclair, Andreas Schuh +8
Counterfactual image generation enables controlled data augmentation, bias mitigation, and disease modeling. However, existing methods guided by external classifiers or regressors…
Pixel-level Counterfactual Contrastive Learning for Medical Image Segmentation
Marceau Lafargue-Hauret, Raghav Mehta, Fabio De Sousa Ribeiro +2
Image segmentation relies on large annotated datasets, which are expensive and slow to produce. Silver-standard (AI-generated) labels are easier to obtain, but they risk introducin…
A Causal Framework for Mitigating Data Shifts in Healthcare
Kurt Butler, Stephanie Riley, Damian Machlanski +13
Developing predictive models that perform reliably across diverse patient populations and heterogeneous environments is a core aim of medical research. However, generalization is o…
Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis
Tian Xia, Matthew Sinclair, Andreas Schuh +8
Counterfactual image generation is a powerful tool for augmenting training data, de-biasing datasets, and modeling disease. Current approaches rely on external classifiers or regre…
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…
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.…
Where are we with calibration under dataset shift in image classification?
Mélanie Roschewitz, Raghav Mehta, Fabio de Sousa Ribeiro +1
We conduct an extensive study on the state of calibration under real-world dataset shift for image classification. Our work provides important insights on the choice of post-hoc an…
CF-Seg: Counterfactuals meet Segmentation
Raghav Mehta, Fabio De Sousa Ribeiro, Tian Xia +4
Segmenting anatomical structures in medical images plays an important role in the quantitative assessment of various diseases. However, accurate segmentation becomes significantly…
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…
Radio-opaque artefacts in digital mammography: automatic detection and analysis of downstream effects
Amelia Schueppert, Ben Glocker, Mélanie Roschewitz
This study investigates the effects of radio-opaque artefacts, such as skin markers, breast implants, and pacemakers, on mammography classification models. After manually annotatin…
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…
Robust image representations with counterfactual contrastive learning
Mélanie Roschewitz, Fabio De Sousa Ribeiro, Tian Xia +2
Contrastive pretraining can substantially increase model generalisation and downstream performance. However, the quality of the learned representations is highly dependent on the d…
Latent Causal Modeling for 3D Brain MRI Counterfactuals
Wei Peng, Tian Xia, Fabio De Sousa Ribeiro +5
The number of samples in structural brain MRI studies is often too small to properly train deep learning models. Generative models show promise in addressing this issue by effectiv…
SharkTrack: an accurate, generalisable software for streamlining shark and ray underwater video analysis
Filippo Varini, Joel H. Gayford, Jeremy Jenrette +11
Elasmobranchs (shark sand rays) represent a critical component of marine ecosystems. Yet, they are experiencing global population declines and effective monitoring of populations 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…