Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ren, Xuhua | - |
dc.contributor.author | Li, Tingting | - |
dc.contributor.author | Yang, Xiujun | - |
dc.contributor.author | Wang, Shuai | - |
dc.contributor.author | Ahmad, Sahar | - |
dc.contributor.author | Xiang, Lei | - |
dc.contributor.author | Stone, Shaun Richard | - |
dc.contributor.author | Li, Lihong | - |
dc.contributor.author | Zhan, Yiqiang | - |
dc.contributor.author | Shen, Dinggang | - |
dc.contributor.author | Wang, Qian | - |
dc.date.accessioned | 2021-09-01T08:18:24Z | - |
dc.date.available | 2021-09-01T08:18:24Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2019-09 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/63414 | - |
dc.description.abstract | Skeletal bone age assessment is a common clinical practice to investigate endocrinology, and genetic and growth disorders of children. However, clinical interpretation and bone age analyses are time-consuming, labor intensive, and often subject to inter-observer variability. This advocates the need of a fully automated method for bone age assessment. We propose a regression convolutional neural network (CNN) to automatically assess the pediatric bone age from hand radiograph. Our network is specifically trained to place more attention to those bone age related regions in the X-ray images. Specifically, we first adopt the attention module to process all images and generate the coarse/fine attention maps as inputs for the regression network. Then, the regression CNN follows the supervision of the dynamic attention loss during training; thus, it can estimate the bone age of the hard (or "outlier") images more accurately. The experimental results show that our method achieves an average discrepancy of 5.2-5.3 months between clinical and automatic bone age evaluations on two large datasets. In conclusion, we propose a fully automated deep learning solution to process X-ray images of the hand for bone age assessment, with the accuracy comparable to human experts but with much better efficiency. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1109/JBHI.2018.2876916 | - |
dc.identifier.scopusid | 2-s2.0-85055215069 | - |
dc.identifier.wosid | 000489729400023 | - |
dc.identifier.bibliographicCitation | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.23, no.5, pp.2030 - 2038 | - |
dc.relation.isPartOf | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.citation.title | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.citation.volume | 23 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 2030 | - |
dc.citation.endPage | 2038 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.subject.keywordAuthor | Bone age assessment | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | regression convolutional neural network | - |
dc.subject.keywordAuthor | hand radiograph | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.