33 citations · 53 across the 6 of their papers we have counts for
7 papers
Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans
Lorenzo Venturini, Samuel Budd, Alfonso Farruggia +6
The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shif…
Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
Hadrien Reynaud, Mengyun Qiao, Mischa Dombrowski +5
Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, ca…
Can non-specialists provide high quality gold standard labels in challenging modalities?
Samuel Budd, Thomas Day, John Simpson +5
Probably yes. -- Supervised Deep Learning dominates performance scores for many computer vision tasks and defines the state-of-the-art. However, medical image analysis lags behind…
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Samuel Budd, Matthew Sinclair, Thomas Day +11
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill requi…
Detecting Outliers with Poisson Image Interpolation
Jeremy Tan, Benjamin Hou, Thomas Day +3
Supervised learning of every possible pathology is unrealistic for many primary care applications like health screening. Image anomaly detection methods that learn normal appearanc…
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome
Elisa Chotzoglou, Thomas Day, Jeremy Tan +5
Congenital heart disease is considered as one the most common groups of congenital malformations which affects per newborns. In this work, an automated framework for…