176 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Imperial College LondonGB100 papers
- King's College LondonGB6 papers
- University of OxfordGB6 papers
- University College LondonGB5 papers
- University of CambridgeGB5 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Technical University of MunichDE4 papers
- Institute for High Energy PhysicsES3 papers
- Institute of Group AnalysisGB3 papers
- Lancaster UniversityGB3 papers
- MRC London Institute of Medical SciencesGB3 papers
- Nanjing University of Science and TechnologyCN3 papers
18 papers · 1 filter
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Revisiting Medical Image Retrieval via Knowledge Consolidation
Yang Nan, Huichi Zhou, Xiaodan Xing +5
As artificial intelligence and digital medicine increasingly permeate healthcare systems, robust governance frameworks are essential to ensure ethical, secure, and effective implem…
SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation
Haozheng Xu, Alistair Weld, Chi Xu +16
Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods…
Robust image representations with counterfactual contrastive learning
Mélanie Roschewitz, Fabio De Sousa Ribeiro, Tian Xia +2
Contrastive pretraining can substantially increase model generalisation and downstream performance. However, the quality of the learned representations is highly dependent on the d…
Test-time generative augmentation for medical image segmentation
Xiao Ma, Yuhui Tao, Zetian Zhang +7
Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusion…
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs
Taha Emre, Arunava Chakravarty, Antoine Rivail +10
Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent famil…
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines
Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik +5
Uncertainty estimation, which provides a means of building explainable neural networks for medical imaging applications, have mostly been studied for single deep learning models th…
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders
Agostina J Larrazabal, César Martínez, Ben Glocker +1
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms.…
A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks
Giacomo Tarroni, Ozan Oktay, Matthew Sinclair +7
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath…
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…
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
Carlo Biffi, Ozan Oktay, Giacomo Tarroni +9
Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular disea…
CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks
Alexandros Kouris, Stylianos I. Venieris, Christos-Savvas Bouganis
This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference. A two-stage architectu…
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
Maximilian Seitzer, Guang Yang, Jo Schlemper +9
Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE)…
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
Martin Rajchl, Nick Pawlowski, Daniel Rueckert +2
NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM. The network is trained on 5…
Deep Segmentation and Registration in X-Ray Angiography Video
Athanasios Vlontzos, Krystian Mikolajczyk
In interventional radiology, short video sequences of vein structure in motion are captured in order to help medical personnel identify vascular issues or plan intervention. Semant…
A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild
Dimitrios Kollias, Stefanos Zafeiriou
This paper presents our approach to the One-Minute Gradual-Emotion Recognition (OMG-Emotion) Challenge, focusing on dimensional emotion recognition through visual analysis of the p…
A Real-time and Registration-free Framework for Dynamic Shape Instantiation
Xiao-Yun Zhou, Guang-Zhong Yang, Su-Lin Lee
Real-time 3D navigation during minimally invasive procedures is an essential yet challenging task, especially when considerable tissue motion is involved. To balance image acquisit…
Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules
Xinyang Feng, Jie Yang, Andrew F. Laine +1
Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for auto…