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

Abstract

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. To explore the model explanations and estimator consensus for "safe zone" prediction. We collected the pelvic datasets from Orthopaedic Hospital, and a novel acetabular cup detection method is proposed for automatic ROI segmentation. Hybrid priors comprising both specific priors from data and general priors from experts are constructed. Specifically, specific priors are constructed based on the fine-tuned ResNet-101 convolutional neural networks (CNN) model, and general priors are constructed based on expert knowledge. Our method considers the model explanations and dynamic consensus through appending a SHapley Additive exPlanations (SHAP) module and a dynamic estimator stacking. The proposed method achieves an accuracy of 99.40% and an area under the curve of 0.9998. Experimental results show that our model achieves superior results to the state-of-the-art conventional ensemble classifiers and deep CNN models. This new screening model provides a new option for the "safe zone" prediction of acetabular cup.