BibTex @article{drukker2022clinical, title={Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep learning-based analysis}, author={Drukker, L and Sharma, H and Karim, JN and Droste, R and Noble, JA and Papageorghiou, AT}, journal={Ultrasound in Obstetrics \& Gynecology}, publisher={Wiley Online Library} }
Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly operator-dependent and difficult to perform, which limits its wider use in clinical practice. The literature on understanding what makes clinical …
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} }
This paper considers automatic clinical workflow description of full-length routine fetal anomaly ultrasound scans using deep learning approaches for spatio-temporal video analysis. Multiple architectures consisting of 2D and 2D + t CNN, LSTM, and …