1 citations · 2 across the 10 of their papers we have counts for
12 papers
Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification
Woo-Jin Cho Kim, Jorge Oliveira, Arian Beqiri +8
Guidelines for transthoracic echocardiographic examination recommend the acquisition of multiple video clips from different views of the heart, resulting in a large number of clips…
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…
Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms
David Stojanovski, Mariana da Silva, Pablo Lamata +2
We investigate the utility of diffusion generative models to efficiently synthesise datasets that effectively train deep learning models for image analysis. Specifically, we propos…
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…
Robustness Testing of Black-Box Models Against CT Degradation Through Test-Time Augmentation
Jack Highton, Quok Zong Chong, Samuel Finestone +3
Deep learning models for medical image segmentation and object detection are becoming increasingly available as clinical products. However, as details are rarely provided about the…
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…
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation
David Stojanovski, Uxio Hermida, Pablo Lamata +2
We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show th…
Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo views
David Stojanovski, Uxio Hermida, Marica Muffoletto +3
Accurate geometric quantification of the human heart is a key step in the diagnosis of numerous cardiac diseases, and in the management of cardiac patients. Ultrasound imaging is t…
D'ARTAGNAN: Counterfactual Video Generation
Hadrien Reynaud, Athanasios Vlontzos, Mischa Dombrowski +4
Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions. We explore this path for the cas…
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…
Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation
Hadrien Reynaud, Athanasios Vlontzos, Benjamin Hou +3
Cardiac ultrasound imaging is used to diagnose various heart diseases. Common analysis pipelines involve manual processing of the video frames by expert clinicians. This suffers fr…