Fetal ultrasound

Gaze-assisted automatic captioning of fetal ultrasound videos using three-way multi-modal deep neural networks

BibTex @article{ALSHARID2022102630, title = {Gaze-assisted automatic captioning of fetal ultrasound videos using three-way multi-modal deep neural networks}, journal = {Medical Image Analysis}, volume = {82}, pages = {102630}, year = {2022}, issn = {1361-8415}, doi = {https://doi.org/10.1016/j.media.2022.102630}, url = {https://www.sciencedirect.com/science/article/pii/S1361841522002584}, author = {Mohammad Alsharid and Yifan Cai and Harshita Sharma and Lior Drukker and Aris T. Papageorghiou and J. Alison Noble}, keywords = {Video captioning, Gaze tracking, Fetal ultrasound, Audio–visual, Multi-modal}, abstract = {In this work, we present a novel gaze-assisted natural language processing (NLP)-based video captioning model to describe routine second-trimester fetal ultrasound scan videos in a vocabulary of spoken sonography.

Weakly Supervised Captioning of Ultrasound Images

Medical image captioning models generate text to describe the semantic contents of an image, aiding the non-experts in understanding and interpretation. We propose a weakly-supervised approach to improve the performance of image captioning models on …

A picture is worth 1000 words: textual analysis of routine 20‐week scan

BibTex @article{alsharid2022picture, title={A picture is worth 1000 words: textual analysis of routine 20-week scan}, author={Alsharid, M and Drukker, L and Sharma, H and Noble, JA and Papageorghiou, AT}, journal={Ultrasound in Obstetrics \& Gynecology}, publisher={Wiley Online Library} }

Visualising Spatio-Temporal Gaze Characteristics for Exploratory Data Analysis in Clinical Fetal Ultrasound Scans

Visualising patterns in clinicians' eye movements while interpreting fetal ultrasound imaging videos is challenging. Across and within videos, there are differences in size and position of Areas-of-Interest (AOIs) due to fetal position, movement and …

Towards Scale and Position Invariant Task Classification Using Normalised Visual Scanpaths in Clinical Fetal Ultrasound

We present a method for classifying tasks in fetal ultrasound scans using the eye-tracking data of sonographers. The visual attention of a sonographer captured by eye-tracking data over time is defined by a scanpath. In routine fetal ultrasound, the …

Multimodal Continual Learning with Sonographer Eye-Tracking in Fetal Ultrasound

Deep networks have been shown to achieve impressive accuracy for some medical image analysis tasks where large datasets and annotations are available. However, tasks involving learning over new sets of classes arriving over extended time is a …

Machine Learning-based Analysis of Operator Pupillary Response to Assess Cognitive Workload in Clinical Ultrasound Imaging

Introduction Pupillometry, the measurement of eye pupil diameter, is a well-established and objective modality correlated with cognitive workload. In this paper, we analyse the pupillary response of ultrasound imaging operators to assess their …

A Course-Focused Dual Curriculum For Image Captioning

We propose a curriculum learning captioning method to caption fetal ultrasound images by training a model to dynamically transition between two different modalities (image and text) as training progresses. Specifically, we propose a course-focused …

OC10.02: Task‐evoked pupillary response as an index of cognitive workload of sonologists undertaking fetal ultrasound

BibTex @article{doi:10.1002/uog.22266, author = {Sharma, H. and Drukker, L. and Droste, R. and Chatelain, P. and Papageorghiou, A.T. and Noble, J.A.}, title = {OC10.02: Task-evoked pupillary response as an index of cognitive workload of sonologists undertaking fetal ultrasound}, journal = {Ultrasound in Obstetrics \& Gynecology}, volume = {56}, number = {S1}, pages = {28-28}, doi = {10.1002/uog.22266}, url = {https://obgyn.onlinelibrary.wiley.com/doi/abs/10.1002/uog.22266}, eprint = {https://obgyn.onlinelibrary.wiley.com/doi/pdf/10.1002/uog.22266}, year = {2020} }

OC10.11: The data science of obstetric ultrasound: automatic analysis of full‐length anomaly scans using machine learning algorithms

BibTex @article{doi:10.1002/uog.22275, author = {Drukker, L. and Sharma, H. and Droste, R. and Noble, J.A. and Papageorghiou, A.T.}, title = {OC10.11: The data science of obstetric ultrasound: automatic analysis of full-length anomaly scans using machine learning algorithms}, journal = {Ultrasound in Obstetrics \& Gynecology}, volume = {56}, number = {S1}, pages = {31-31}, doi = {10.1002/uog.22275}, url = {https://obgyn.onlinelibrary.wiley.com/doi/abs/10.1002/uog.22275}, eprint = {https://obgyn.onlinelibrary.wiley.com/doi/pdf/10.1002/uog.22275}, year = {2020} }