30 citations · 60 across the 17 of their papers we have counts for
20 papers
Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography
Yingyu Yang, Qianye Yang, Can Peng +4
Fetal echocardiography is essential for detecting congenital heart disease (CHD), facilitating pregnancy management, optimized delivery planning, and timely postnatal interventions…
Learning to learn skill assessment for fetal ultrasound scanning
Yipei Wang, Qianye Yang, Lior Drukker +3
Traditionally, ultrasound skill assessment has relied on expert supervision and feedback, a process known for its subjectivity and time-intensive nature. Previous works on quantita…
Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
Yingyu Yang, Qianye Yang, Kangning Cui +6
The identification of cardiac phase is an essential step for analysis and diagnosis of cardiac function. Automatic methods, especially data-driven methods for cardiac phase detecti…
Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation
Divyanshu Mishra, Mohammadreza Salehi, Pramit Saha +4
Self-supervised learning (SSL) has achieved major advances in natural images and video understanding, but challenges remain in domains like echocardiography (heart ultrasound) due…
MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
Divyanshu Mishra, Pramit Saha, He Zhao +4
Accurate standard plane acquisition in fetal ultrasound (US) videos is crucial for fetal growth assessment, anomaly detection, and adherence to clinical guidelines. However, manual…
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
Pramit Saha, Divyanshu Mishra, Netzahualcoyotl Hernandez-Cruz +4
Congenital Heart Disease (CHD) is one of the leading causes of fetal mortality, yet the scarcity of labeled CHD data and strict privacy regulations surrounding fetal ultrasound (US…
Pose-GuideNet: Automatic Scanning Guidance for Fetal Head Ultrasound from Pose Estimation
Qianhui Men, Xiaoqing Guo, Aris T. Papageorghiou +1
3D pose estimation from a 2D cross-sectional view enables healthcare professionals to navigate through the 3D space, and such techniques initiate automatic guidance in many image-g…
Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos
Divyanshu Mishra, He Zhao, Pramit Saha +2
Out-of-distribution (OOD) detection is essential to improve the reliability of machine learning models by detecting samples that do not belong to the training distribution. Detecti…
Show from Tell: Audio-Visual Modelling in Clinical Settings
Jianbo Jiao, Mohammad Alsharid, Lior Drukker +3
Auditory and visual signals usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, the audio-visual modell…
Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound
Zeyu Fu, Jianbo Jiao, Robail Yasrab +3
Self-supervised contrastive representation learning offers the advantage of learning meaningful visual representations from unlabeled medical datasets for transfer learning. Howeve…
Multimodal-GuideNet: Gaze-Probe Bidirectional Guidance in Obstetric Ultrasound Scanning
Qianhui Men, Clare Teng, Lior Drukker +2
Eye trackers can provide visual guidance to sonographers during ultrasound (US) scanning. Such guidance is potentially valuable for less experienced operators to improve their scan…
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…
Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis
Jianbo Jiao, Ana I. L. Namburete, Aris T. Papageorghiou +1
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for second-trimester anomaly screening, for which ultrasound (US) i…
Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound
Jianbo Jiao, Yifan Cai, Mohammad Alsharid +3
In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a resul…
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…
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…
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…
Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks
Felipe Moser, Ruobing Huang, Aris T. Papageorghiou +2
To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain deve…
Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images
Jianbo Jiao, Ana I. L. Namburete, Aris T. Papageorghiou +1
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for anomaly screening. For this ultrasound (US) is employed. While…
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention
Richard Droste, Yifan Cai, Harshita Sharma +4
Image representations are commonly learned from class labels, which are a simplistic approximation of human image understanding. In this paper we demonstrate that transferable repr…