30 citations · 60 across the 17 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
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…
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…