PhD opportunity on "Semi-supervised detection and tracking of instruments for robotic surgery guidance"

This project seeks to advance the state of the art in AI-based surgical tool detection and tracking by designing novel semi-supervised and weakly-supervised approaches able to achieve robust and real-time performance.

Representative sample images of robotic surgery (left) and state-of-the-art instrument segmentation results (right). True positive (white), true negative (black), false positive (magenta), and false negative (green).

Aim of the PhD Project:

  • Robust, real-time detection and tracking of surgical tools
  • Learning from combined small-scale annotated and large-scale but non-annotated datasets of robotic surgery video footages
  • Advancing the state of the art in combining self-supervision, week-supervision, and semi-supervision for surgical vision tasks
  • Designing and validating stereo-vision based learning paradigms

1st Supervisor: Tom Vercauteren, King’s College London

2nd Supervisor: Miaojing Shi, King’s College London

Clinical Champion: Prokar Dasgupta, King’s College London

More information about the PhD project here.

Tom Vercauteren
Tom Vercauteren
Professor of Interventional Image Computing

Tom’s research interests include machine learning and computer assisted interventions

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