204 citations · 228 across the 24 of their papers we have counts for
9 papers · 1 filter
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…