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Richard Droste

Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep learning-based analysis
Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
Knowledge Representation and Learning of Operator Clinical Workflow from Full-length Routine Fetal Ultrasound Scan Videos
OC10.02: Task‐evoked pupillary response as an index of cognitive workload of sonologists undertaking fetal ultrasound
OC10.11: The data science of obstetric ultrasound: automatic analysis of full‐length anomaly scans using machine learning algorithms
Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking
Spatio-Temporal Visual Attention Modelling of Standard Biometry Plane-Finding Navigation
Discovering Salient Anatomical Landmarks by Predicting Human Gaze
Spatio-Temporal Partitioning And Description Of Full-Length Routine Fetal Anomaly Ultrasound Scans
Towards Capturing Sonographic Experience: Cognition-Inspired Ultrasound Video Saliency Prediction
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention
Monitoring Sonographer Performance: The Perception Ultrasound by Learning Sonographer Experience (PULSE) study