6 Result(s)
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Chapter and Conference Paper
Deep Structural Causal Shape Models
Causal reasoning provides a language to ask important interventional and counterfactual questions beyond purely statistical association. In medical imaging, for example, we may want to study the causal effect ...
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Chapter and Conference Paper
Making the Most of Text Semantics to Improve Biomedical Vision–Language Processing
Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical research. Biomedical text with ...
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Chapter and Conference Paper
Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing
We investigate discrete spin transformations, a geometric framework to manipulate surface meshes by controlling mean curvature. Applications include surface fairing – flowing a mesh onto say, a reference spher...
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Chapter and Conference Paper
Bayesian Deep Learning for Accelerated MR Image Reconstruction
Recently, many deep learning (DL) based MR image reconstruction methods have been proposed with promising results. However, only a handful of work has been focussing on characterising the behaviour of deep networ...
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Chapter and Conference Paper
Nonparametric Density Flows for MRI Intensity Normalisation
With the adoption of powerful machine learning methods in medical image analysis, it is becoming increasingly desirable to aggregate data that is acquired across multiple sites. However, the underlying assumpt...
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Chapter and Conference Paper
Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning
Reconstructing magnetic resonance imaging (MRI) from undersampled k-space enables the accelerated acquisition of MRI but is a challenging problem. However, in many diagnostic scenarios, perfect reconstructions ar...
