no code implementations • 30 Jul 2021 • Samuel Budd, Thomas Day, John Simpson, Karen Lloyd, Jacqueline Matthew, Emily Skelton, Reza Razavi, Bernhard Kainz
We study the time and cost implications of using novice annotators, the raw performance of novice annotators compared to gold-standard expert annotators, and the downstream effects on a trained Deep Learning segmentation model's performance for detecting a specific congenital heart disease (hypoplastic left heart syndrome) in fetal ultrasound imaging.
no code implementations • 6 Jul 2021 • Samuel Budd, Matthew Sinclair, Thomas Day, Athanasios Vlontzos, Jeremy Tan, Tianrui Liu, Jaqueline Matthew, Emily Skelton, John Simpson, Reza Razavi, Ben Glocker, Daniel Rueckert, Emma C. Robinson, Bernhard Kainz
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts.
1 code implementation • 6 Jul 2021 • Jeremy Tan, Benjamin Hou, Thomas Day, John Simpson, Daniel Rueckert, Bernhard Kainz
We propose an alternative to image reconstruction-based and image embedding-based methods and propose a new self-supervised method to tackle pathological anomaly detection.
no code implementations • 15 Nov 2020 • Elisa Chotzoglou, Thomas Day, Jeremy Tan, Jacqueline Matthew, David Lloyd, Reza Razavi, John Simpson, Bernhard Kainz
Congenital heart disease is considered as one the most common groups of congenital malformations which affects $6-11$ per $1000$ newborns.
no code implementations • 16 Aug 2020 • Jeremy Tan, Anselm Au, Qingjie Meng, Sandy FinesilverSmith, John Simpson, Daniel Rueckert, Reza Razavi, Thomas Day, David Lloyd, Bernhard Kainz
In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound.