13 citations · 48 across the 8 of their papers we have counts for
9 papers
Unsupervised 3D out-of-distribution detection with latent diffusion models
Mark S. Graham, Walter Hugo Lopez Pinaya, Paul Wright +8
Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilis…
Individualized prescriptive inference in ischaemic stroke
Dominic Giles, Chris Foulon, Guilherme Pombo +8
The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials, based on simple descriptions of presumptively homogeneous populations.…
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
Walter H. L. Pinaya, Mark S. Graham, Robert Gray +12
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive trans…
Transformer-based out-of-distribution detection for clinically safe segmentation
Mark S Graham, Petru-Daniel Tudosiu, Paul Wright +9
In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently…
Towards Quantifying Neurovascular Resilience
Stefano Moriconi, Rafael Rehwald, Maria A. Zuluaga +4
Whilst grading neurovascular abnormalities is critical for prompt surgical repair, no statistical markers are currently available for predicting the risk of adverse events, such as…
Let's agree to disagree: learning highly debatable multirater labelling
Carole H. Sudre, Beatriz Gomez Anson, Silvia Ingala +9
Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a…