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Deep learning for surgical phase recognition in laparoscopic hiatal hernia repair.

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Summary: Automated surgical phase recognition is a useful technology for processing video recordings of surgical procedures for a variety of uses, including education, skill assessment, quality improvement, and coaching. Deep learning has been successfully applied to surgical phase recognition for multiple procedures, including cholecystectomy, sleeve gastrectomy, sigmoidectomy, and endoscopic myotomy. However, it has not yet been applied to hiatal hernia repair. Our goal is to develop a deep learning-based system for automatic phase recognition for laparoscopic hiatal hernia repair.

Pre-requisite: We are looking for students who have experience in training deep neural networks for any computer vision tasks.

If you are interested please email Please send me your CV + include description of deep learning projects you have participated (bonus if you can show some examples of the project, e.g. a github repo).

We will prioritize Juniors, since the project may continue into the next academic year. There may be hourly pay options for Summer, contingent upon progress and funding.

Faculty Advisor
Soumyadip (Roni) Sengupta (CS) + Timothy Farrell (Med School)
Research Supervisor
Kevin Chen
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Type of Position
Application Deadline