2 citations · 2 across the 3 of their papers we have counts for
5 papers
Uncertainty as a Proxy for Semantic Correctness in Diffusion-Based Medical Image Synthesis
Yuxuan Ou, Konstantinos Kamnitsas, OxAAA Study +3
Diffusion models can synthesise contrast-enhanced CT (CECT) from non-contrast CT (NCCT), avoiding contrast administration and its environmental and patient-access costs. However, v…
EchoLVFM: One-Step Video Generation via Latent Flow Matching for Echocardiogram Synthesis
Emmanuel Oladokun, Sarina Thomas, Jurica Šprem +1
Echocardiography is widely used for assessing cardiac function, where clinically meaningful parameters such as left-ventricular ejection fraction (EF) play a central role in diagno…
AortaDiff: A Unified Multitask Diffusion Framework For Contrast-Free AAA Imaging
Yuxuan Ou, Ning Bi, Jiazhen Pan +5
While contrast-enhanced CT (CECT) is standard for assessing abdominal aortic aneurysms (AAA), the required iodinated contrast agents pose significant risks, including nephrotoxicit…
From Transthoracic to Transesophageal: Cross-Modality Generation using LoRA Diffusion
Emmanuel Oladokun, Yuxuan Ou, Anna Novikova +4
Deep diffusion models excel at realistic image synthesis but demand large training sets-an obstacle in data-scarce domains like transesophageal echocardiography (TEE). While synthe…
Transesophageal Echocardiography Generation using Anatomical Models
Emmanuel Oladokun, Musa Abdulkareem, Jurica Šprem +1
Through automation, deep learning (DL) can enhance the analysis of transesophageal echocardiography (TEE) images. However, DL methods require large amounts of high-quality data to…