most citedRSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations

126 citations · 219 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2021★ 3 cited

Curriculum Design for Teaching via Demonstrations: Theory and Applications

Gaurav Yengera, Rati Devidze, Parameswaran Kamalaruban +1

We consider the problem of teaching via demonstrations in sequential decision-making settings. In particular, we study how to design a personalized curriculum over demonstrations t…

cs.CV2018★ 38 cited

Future-State Predicting LSTM for Early Surgery Type Recognition

Siddharth Kannan, Gaurav Yengera, Didier Mutter +2

This work presents a novel approach for the early recognition of the type of a laparoscopic surgery from its video. Early recognition algorithms can be beneficial to the developmen…

cs.CV2018★ 51 cited

Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks

Gaurav Yengera, Didier Mutter, Jacques Marescaux +1

Real-time algorithms for automatically recognizing surgical phases are needed to develop systems that can provide assistance to surgeons, enable better management of operating room…

cs.CV2018★ 126 cited

RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations

Andru Putra Twinanda, Gaurav Yengera, Didier Mutter +2

Accurate surgery duration estimation is necessary for optimal OR planning, which plays an important role in patient comfort and safety as well as resource optimization. It is, howe…

eess.SY2018★ 1 cited

Computation of Extended Robust Kalman Filter for Real-Time Attitude and Position Estimation

Gaurav Yengera, Roberto Inoue, Mundla Narasimhappa +1

This paper deals with the implementation of the extended robust Kalman filter (ERKF) which was developed considering uncertainties in the parameter matrices of the underlying state…