EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

@article{Twinanda2017EndoNetAD,
  title={EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos},
  author={Andru Putra Twinanda and Sherif Shehata and Didier Mutter and Jacques Marescaux and Michel de Mathelin and Nicolas Padoy},
  journal={IEEE Transactions on Medical Imaging},
  year={2017},
  volume={36},
  pages={86-97}
}
Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals… CONTINUE READING
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