D. Rueckert

Latest

• Metadata-enhanced contrastive learning from retinal optical coherence tomography images.
• Reconciling privacy and accuracy in AI for medical imaging.
• Evaluating and mitigating limitations of large language models in clinical decision making.
• Unsupervised Pathology Detection: A Deep Dive Into the State of the Art.
• Evaluating Normative Representation Learning in Generative AI for Robust Anomaly Detection in Brain Imaging.
• Federated electronic health records for the European Health.
• A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks.
• Interactive and Explainable Region-guided Radiology Report Generation.
• Concurrent ischemic lesion age estimation and segmentation of CT brain using a Transformer-based network.
• DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning.
• Neural Implicit k-Space for Binning-Free Non-Cartesian Cardiac MR Imaging.
• Best of Both Worlds: Multimodal Contrastive Learning With Tabular and Imaging Data.
• Causality-inspired Single-source Domain Generalization for Medical Image Segmentation.
• Enhancing MR image segmentation with realistic adversarial data augmentation.
• Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior.
• Self-supervised Learning for Few-shot Medical Image Segmentation.
• Private Graph Neural Networks for Whole-Graph Classification.
• The Developing Human Connectome Project Neonatal Data Release.
• Adversarial interference and its mitigations in privacy-preserving collaborative machine learning.
• A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies with Progress Highlights, and Future Promises.
• Federated deep learning for detecting COVID-19 lung abnormalities in CT: A privacy-preserving multinational validation study.