Odysseas Zisimopoulos

TL;DR: In this paper, the authors adopt a deep learning paradigm to detect surgical instruments in cataract surgery videos which in turn feed a surgical phase inference recurrent network that encodes temporal aspects of phase steps within the phase classification.

TL;DR: Evaluating tool annotation algorithms based on deep learning for cataract surgery finds that the quality of their annotations are compared to that of human interpretations, and it is expected that they will guide the design of efficient surgery monitoring tools in the near future.

TL;DR: Capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool for bowel cancers including colorectal cancer (CRC) and may provide real-time information about lesionigenicity and prognosis.

TL;DR: This work adopts a deep learning paradigm to detect surgical instruments in cataract surgery videos which in turn feed a surgical phase inference recurrent network that encodes temporal aspects of phase steps within the phase classification.

TL;DR: This is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data and indicates that simulated data does have some potential for training advanced classification methods for CAI systems.