4 papers
To do() or not to do(): Medical Image Counterfactuals for Dataset Augmentation
Yasin Ibrahim, Robin J. Evans, Konstantinos Kamnitsas
Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy for mitigating such biases is…
Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables
Yasin Ibrahim, Hermione Warr, Robin J. Evans +1
Machine learning models can achieve strong test performance while relying on demographic or acquisition-related shortcuts. We propose counterfactual (CF) marginalisation as a test-…
Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components
Hermione Warr, Yasin Ibrahim, Daniel R. McGowan +1
Automation of medical image interpretation could alleviate bottlenecks in diagnostic workflows, and has become of particular interest in recent years due to advancements in natural…
Semi-Supervised Learning for Deep Causal Generative Models
Yasin Ibrahim, Hermione Warr, Konstantinos Kamnitsas
Developing models that are capable of answering questions of the form "How would x change if y had been z?'" is fundamental to advancing medical image analysis. Training causal gen…