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Richard Droste

PhD Student in Medical Imaging

University of Oxford

Biography

Richard Droste graduated with distinction from ETH Zurich with B.Sc. (2014) and M.Sc.(2017) degrees in mechanical engineering. Before commencing his Ph.D. in engineering science at the University of Oxford in 2017, he worked as an intern at Siemens Healthcare, Germany, in 2015, at McKinsey & Company, Germany, in 2016, and as a research assistant at the Institute for Biomedical Engineering, ETH Zurich in 2017.

Currently, Richard is working on the interdisciplinary PULSE (Perception Ultrasound by Learning Sonographic Experience) project. The aim of the project is to build machine learning systems that capture the expertise of sonographers in order to enable the widespread deployment of fetal ultrasound screenings.

Interests

  • Machine Learning & Deep Learning
  • Computer Vision
  • Medical Imaging (Ultrasund & MRI)

Education

  • Currently: PhD in Engineering Science, (graduating 2021)

    University of Oxford

  • MSC in Mechanical Engineering, 2017

    ETH Zurich

  • BSc in Mechanical Engineering, 2014

    ETH Zurich

Publications

Unified Image and Video Saliency Modeling

arXiv preprint. *RD and JJ contributed equally to this work.

Safety Indices of Ultrasound: Adherence to Recommendations and Awareness During Routine Obstetric Ultrasound Scanning

European Journal of Ultrasound (Ultraschall in der Medizin). *LD and RD contributed equally to this work.

Discovering Salient Anatomical Landmarks by Predicting Human Gaze

IEEE International Symposium on Biomedical Imaging (ISBI) 2020. Oral presentation.

Self-supervised Representation Learning for Ultrasound Video

IEEE International Symposium on Biomedical Imaging (ISBI) 2020.

Expected‐value bias in routine third‐trimester growth scans

Ultrasound in Obstetrics & Gynecology. *LD and RD contributed equally to this work.

OC10.02: Bioeffects safety indices of ultrasound: quantifying adherence to recommendations on routine obstetric scan

ISUOG World Congress 2019. Oral presentation. *LD and RD contributed equally to this work.

OC19.02: A novel eye tracking study: how common is expected value bias in fetal growth scan assessment?

ISUOG World Congress 2019. Oral presentation. *LD and RD contributed equally to this work.

Towards Capturing Sonographic Experience: Cognition-Inspired Ultrasound Video Saliency Prediction

23rd Conference on Medical Image Understanding and Analysis (MIUA) 2019. Oral presentation. Best paper award.

Spatio-temporal Partitioning and Description of Full-length Routine Fetal Anomaly Ultrasound Scans

IEEE International Symposium on Biomedical Imaging (ISBI) 2019. Oral presentation.

Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention

26th International conference on Information Processing in Medical Imaging (IPMI) 2019.