  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).
