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Learning Invariant Representation of Tasks for Robust Surgical State Estimation
The combination of high diversity and limited data calls for new learning methods that are robust and invariant to operating conditions and surgical techniques.
daVinciNet: Joint Prediction of Motion and Surgical State in Robot-Assisted Surgery
We propose daVinciNet - an end-to-end dual-task model for robot motion and surgical state predictions.
Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources
A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs.
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