Integrating model explanations and hybrid priors into deep stacked networks for the "safe zone" prediction of acetabular cup.

Background: Existing state-of-the-art "safe zone" prediction methods are statistics-based methods, image-matching techniques, and machine learning methods. Yet, those methods bring a tension between accuracy and interpretability.

Purpose: To explore the model explanations and estimator consensus for "safe zone" prediction.