200 citations · 476 across the 18 of their papers we have counts for
19 papers
Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling
Thierry Judge, Olivier Bernard, Woo-Jin Cho Kim +5
Echocardiography plays a fundamental role in the extraction of clinical parameters (e.g. left ventricular volume and ejection fraction) required to determine the presence and sever…
Multi-Site Class-Incremental Learning with Weighted Experts in Echocardiography
Kit M. Bransby, Woo-jin Cho Kim, Jorge Oliveira +4
Building an echocardiography view classifier that maintains performance in real-life cases requires diverse multi-site data, and frequent updates with newly available data to mitig…
BackMix: Mitigating Shortcut Learning in Echocardiography with Minimal Supervision
Kit Mills Bransby, Arian Beqiri, Woo-Jin Cho Kim +3
Neural networks can learn spurious correlations that lead to the correct prediction in a validation set, but generalise poorly because the predictions are right for the wrong reaso…
Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography
Alberto Gomez, Mihaela Porumb, Angela Mumith +5
We propose a new method to automatically contour the left ventricle on 2D echocardiographic images. Unlike most existing segmentation methods, which are based on predicting segment…
CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation
Thierry Judge, Olivier Bernard, Mihaela Porumb +3
Accurate uncertainty estimation is a critical need for the medical imaging community. A variety of methods have been proposed, all direct extensions of classification uncertainty e…
Contrastive Learning for View Classification of Echocardiograms
Agisilaos Chartsias, Shan Gao, Angela Mumith +4
Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function. Its increasing automation frequently employs deep l…
Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
Haochuan Jiang, Chengjia Wang, Agisilaos Chartsias +1
Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR) images plays a significant role in diagnosis and management for a variety of cardiac diseases. However, the…
Semi-supervised Pathology Segmentation with Disentangled Representations
Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang +6
Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining…
Disentangled Representations for Domain-generalized Cardiac Segmentation
Xiao Liu, Spyridon Thermos, Agisilaos Chartsias +2
Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains. S…
Measuring the Biases and Effectiveness of Content-Style Disentanglement
Xiao Liu, Spyridon Thermos, Gabriele Valvano +3
A recent spate of state-of-the-art semi- and un-supervised solutions disentangle and encode image "content" into a spatial tensor and image appearance or "style" into a vector, to…
Pseudo-healthy synthesis with pathology disentanglement and adversarial learning
Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris
Pseudo-healthy synthesis is the task of creating a subject-specific `healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and under…
Learning to synthesise the ageing brain without longitudinal data
Tian Xia, Agisilaos Chartsias, Chengjia Wang +1
How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data)…
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang +4
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here…
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
Gabriele Valvano, Agisilaos Chartsias, Andrea Leo +1
There has been an increasing focus in learning interpretable feature representations, particularly in applications such as medical image analysis that require explainability, whils…
Conditioning Convolutional Segmentation Architectures with Non-Imaging Data
Grzegorz Jacenków, Agisilaos Chartsias, Brian Mohr +1
We compare two conditioning mechanisms based on concatenation and feature-wise modulation to integrate non-imaging information into convolutional neural networks for segmentation o…
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Chengjia Wang, Giorgos Papanastasiou, Agisilaos Chartsias +3
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway. This paper presents an unsupervised deep inter-modali…
Disentangled Representation Learning in Cardiac Image Analysis
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou +4
Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Reson…
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization
Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris
Pseudo healthy synthesis, i.e. the creation of a subject-specific `healthy' image from a pathological one, could be helpful in tasks such as anomaly detection, understanding change…
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou +5
The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical informat…