66 citations · 235 across the 19 of their papers we have counts for
22 papers
CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI
Liu Li, Qiang Ma, Matthew Sinclair +6
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in…
Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption
George-Liviu Pereteanu, Amir Alansary, Jonathan Passerat-Palmbach
This work presents a novel protocol for fast secure inference of neural networks applied to computer vision applications. It focuses on improving the overall performance of the onl…
CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs
Qiang Ma, Liu Li, Emma C. Robinson +3
We present CortexODE, a deep learning framework for cortical surface reconstruction. CortexODE leverages neural ordinary differential equations (ODEs) to deform an input surface in…
FedRAD: Federated Robust Adaptive Distillation
Stefán Páll Sturluson, Samuel Trew, Luis Muñoz-González +4
The robustness of federated learning (FL) is vital for the distributed training of an accurate global model that is shared among large number of clients. The collaborative learning…
PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction
Qiang Ma, Emma C. Robinson, Bernhard Kainz +2
Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Networ…
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
Loic Le Folgoc, Vasileios Baltatzis, Amir Alansary +8
Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as…
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images
Guy Leroy, Daniel Rueckert, Amir Alansary
Accurate detection of anatomical landmarks is an essential step in several medical imaging tasks. We propose a novel communicative multi-agent reinforcement learning (C-MARL) syste…
Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface
Vitalis Vosylius, Andy Wang, Cemlyn Waters +8
Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the po…
Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare
Matei Grama, Maria Musat, Luis Muñoz-González +3
Federated learning (FL) has enabled training models collaboratively from multiple data owning parties without sharing their data. Given the privacy regulations of patient's healthc…
Bayesian Sampling Bias Correction: Training with the Right Loss Function
L. Le Folgoc, V. Baltatzis, A. Alansary +8
We derive a family of loss functions to train models in the presence of sampling bias. Examples are when the prevalence of a pathology differs from its sampling rate in the trainin…
Flexible Conditional Image Generation of Missing Data with Learned Mental Maps
Benjamin Hou, Athanasios Vlontzos, Amir Alansary +2
Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic. In clinical practice it is frequently…
Multiple Landmark Detection using Multi-Agent Reinforcement Learning
Athanasios Vlontzos, Amir Alansary, Konstantinos Kamnitsas +2
The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a te…
Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing
Loic Le Folgoc, Daniel C. Castro, Jeremy Tan +5
We investigate discrete spin transformations, a geometric framework to manipulate surface meshes by controlling mean curvature. Applications include surface fairing -- flowing a me…
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Amir Alansary, Loic Le Folgoc, Ghislain Vaillant +11
We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to pe…
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
Yuanwei Li, Bishesh Khanal, Benjamin Hou +8
Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring…
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network
Yuanwei Li, Amir Alansary, Juan J. Cerrolaza +7
We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn t…
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…
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images
Benjamin Hou, Bishesh Khanal, Amir Alansary +7
Limited capture range, and the requirement to provide high quality initialization for optimization-based 2D/3D image registration methods, can significantly degrade the performance…
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
Benjamin Hou, Amir Alansary, Steven McDonagh +6
This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range and need for very good initialization of state-of-the…
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging
Steven McDonagh, Benjamin Hou, Konstantinos Kamnitsas +5
3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast but low-resolution image acquisition and highly detailed but slow image acquisition. Fast imaging is required…
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
Amir Alansary, Bernhard Kainz, Martin Rajchl +8
In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to cur…
Learning under Distributed Weak Supervision
Martin Rajchl, Matthew C. H. Lee, Franklin Schrans +7
The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater,…