Machine learning

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} }

Discovering Salient Anatomical Landmarks by Predicting Human Gaze

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 a given image are then annotated manually or via machine learning …

Monitoring Sonographer Performance: The Perception Ultrasound by Learning Sonographer Experience (PULSE) study

Appearance-based necrosis detection using textural features and SVM with discriminative thresholding in histopathological whole slide images

Automatic detection of necrosis in histological images is an interesting problem of digital pathology that needs to be addressed. Determination of presence and extent of necrosis can provide useful information for disease diagnosis and prognosis, and …