200 citations · 476 across the 18 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
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…