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 …
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 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 …
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 …
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 …
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 …
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 …
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} }
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} }
We present a novel curriculum learning approach to train a natural language processing (NLP) based fetal ultrasound image captioning model. Datasets containing medical images and corresponding textual descriptions are relatively rare and hence, …