Learning Invariant Representation of Tasks for Robust Surgical State Estimation

TL;DR

This paper introduces StiseNet, a novel adversarial network that learns invariant representations for surgical state estimation, improving robustness across diverse surgical techniques and environments in robot-assisted surgery datasets.

Contribution

The paper presents StiseNet, a new adversarial architecture that enhances surgical state estimation by reducing sensitivity to variations in surgical techniques and operating conditions.

Findings

StiseNet outperforms existing methods on three datasets.

It effectively isolates nuisance factors from relevant surgical information.

Demonstrates robustness in real-world surgical environments.

Abstract

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).