64 citations
- Imperial College LondonGB3 papers
- Brigham and Women's HospitalUS2 papers
- King's College LondonGB2 papers
- Onze Lieve Vrouwziekenhuis HospitalBE2 papers
- British Heart FoundationGB1 paper
- Cornell UniversityUS1 paper
- Guy's and St Thomas' NHS Foundation TrustGB1 paper
- Harvard UniversityUS1 paper
- Heriot-Watt UniversityGB1 paper
- IIT@MITUS1 paper
- Institut national de recherche en sciences et technologies du numériqueFR1 paper
- Karolinska InstitutetSE1 paper
7 papers
Understanding-informed Bias Mitigation for Fair CMR Segmentation
Tiarna Lee, Esther Puyol-Antón, Bram Ruijsink +6
Artificial intelligence (AI) is increasingly being used for medical imaging tasks. However, there can be biases in AI models, particularly when they are trained using imbalanced tr…
A Diffusion Model for Simulation Ready Coronary Anatomy with Morpho-skeletal Control
Karim Kadry, Shreya Gupta, Jonas Sogbadji +6
Virtual interventions enable the physics-based simulation of device deployment within coronary arteries. This framework allows for counterfactual reasoning by deploying the same de…
Morphology-based non-rigid registration of coronary computed tomography and intravascular images through virtual catheter path optimization
Karim Kadry, Abhishek Karmakar, Andreas Schuh +6
Coronary computed tomography angiography (CCTA) provides 3D information on obstructive coronary artery disease, but cannot fully visualize high-resolution features within the vesse…
Image-and-Spatial Transformer Networks for Structure-Guided Image Registration
Matthew C. H. Lee, Ozan Oktay, Andreas Schuh +2
Image registration with deep neural networks has become an active field of research and exciting avenue for a long standing problem in medical imaging. The goal is to learn a compl…
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
Jo Schlemper, Ozan Oktay, Michiel Schaap +4
We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs…
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
Benjamin Hou, Nina Miolane, Bishesh Khanal +7
Pose estimation, i.e. predicting a 3D rigid transformation with respect to a fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image analysis with application…