57 citations · 93 across the 5 of their papers we have counts for
7 papers
Learning Invariant Representation of Tasks for Robust Surgical State Estimation
Yidan Qin, Max Allan, Yisong Yue +2
Surgical state estimators in robot-assisted surgery (RAS) - especially those trained via learning techniques - rely heavily on datasets that capture surgeon actions in laboratory o…
Stereo Correspondence and Reconstruction of Endoscopic Data Challenge
Max Allan, Jonathan Mcleod, Congcong Wang +21
The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China. The task was to perform…
daVinciNet: Joint Prediction of Motion and Surgical State in Robot-Assisted Surgery
Yidan Qin, Seyedshams Feyzabadi, Max Allan +2
This paper presents a technique to concurrently and jointly predict the future trajectories of surgical instruments and the future state(s) of surgical subtasks in robot-assisted s…
Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources
Yidan Qin, Sahba Aghajani Pedram, Seyedshams Feyzabadi +4
Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an ob…
2018 Robotic Scene Segmentation Challenge
Max Allan, Satoshi Kondo, Sebastian Bodenstedt +38
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward ki…
2017 Robotic Instrument Segmentation Challenge
Max Allan, Alex Shvets, Thomas Kurmann +16
In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand t…