most citedSelf-supervised Representation Learning for Ultrasound Video

6 citations · 6 across the 2 of their papers we have counts for

collaborators

5 papers

eess.IV2021

A Dual Adversarial Calibration Framework for Automatic Fetal Brain Biometry

Yuan Gao, Lok Hin Lee, Richard Droste +4

This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries. Specifically, we leverage high-end (HE) ultrasound i…

eess.IV2020

Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

Richard Droste, Lior Drukker, Aris T. Papageorghiou +1

We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contr…

cs.CV2020

Unified Image and Video Saliency Modeling

Richard Droste, Jianbo Jiao, J. Alison Noble

Visual saliency modeling for images and videos is treated as two independent tasks in recent computer vision literature. While image saliency modeling is a well-studied problem and…

cs.CV20206 cited

Self-supervised Representation Learning for Ultrasound Video

Jianbo Jiao, Richard Droste, Lior Drukker +2

Recent advances in deep learning have achieved promising performance for medical image analysis, while in most cases ground-truth annotations from human experts are necessary to tr…

cs.CV2020

Discovering Salient Anatomical Landmarks by Predicting Human Gaze

Richard Droste, Pierre Chatelain, Lior Drukker +3

Anatomical landmarks are a crucial prerequisite for many medical imaging tasks. Usually, the set of landmarks for a given task is predefined by experts. The landmark locations for…