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

Author:

Han Fuchang1,Liao Shenghui1ORCID,Bai Sifan1,Wu Renzhong1,Zhang Yingqi2,Hao Yongqiang3

Affiliation:

1. School of Computer Science and Engineering, Central South University, Changsha, PR China

2. Tongji Hospital, School of Medicine, Tongji University, Shanghai, PR China

3. Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China

Abstract

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. Material and Methods 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. Results 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. Conclusion This new screening model provides a new option for the “safe zone” prediction of acetabular cup.

Funder

National Natural Science Foundation of China

National Key R & D Program of China

Postgraduate Research and Innovation Project of Hunan

Fundamental Research Funds for the Central Universities of Central South University

Publisher

SAGE Publications

Subject

Radiology, Nuclear Medicine and imaging,General Medicine,Radiological and Ultrasound Technology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3