30 citations · 39 across the 5 of their papers we have counts for
6 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…
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…
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…
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…