Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published April 2021 | public
Journal Article Open

Learning Invariant Representation of Tasks for Robust Surgical State Estimation


Surgical state estimators in robot-assisted surgery (RAS)-especially those trained via learning techniques-rely heavily on datasets that capture surgeon actions in laboratory or real-world surgical tasks. Real-world RAS datasets are costly to acquire, are obtained from multiple surgeons who may use different surgical strategies, and are recorded under uncontrolled conditions in highly complex environments. The combination of high diversity and limited data calls for new learning methods that are robust and invariant to operating conditions and surgical techniques. We propose StiseNet , a Surgical Task Invariance State Estimation Network with an invariance induction framework that minimizes the effects of variations in surgical technique and operating environments inherent to RAS datasets. StiseNet's adversarial architecture learns to separate nuisance factors from information needed for surgical state estimation. StiseNet is shown to outperform state-of-the-art state estimation methods on three datasets (including a new real-world RAS dataset: HERNIA-20).

Additional Information

© 2021 IEEE. Manuscript received October 15, 2020; accepted February 16, 2021. Date of publication March 2, 2021; date of current version March 22, 2021. This letter was recommended for publication by Associate Editor P. Valdastri and Editor Z. Li upon evaluation of the reviewers' comments. This work was supported by Intuitive Surgical Inc. We would like to thank Dr. Seyedshams Feyzabadi, Dr. Azad Shademan, Dr. Sandra Park, Dr. Humphrey Chow, and Dr. Wenqing Sun for their support.

Attached Files

Accepted Version - 2102.09119.pdf


Files (8.4 MB)
Name Size Download all
8.4 MB Preview Download

Additional details

August 20, 2023
August 20, 2023