MRI-based Diagnosis of Rotator Cuff Tears using Deep Learning and Weighted Linear Combinations
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, M. | - |
dc.contributor.author | Park, H.-M. | - |
dc.contributor.author | Kim, J.Y. | - |
dc.contributor.author | Kim, S.H. | - |
dc.contributor.author | Van, Hoeke S. | - |
dc.contributor.author | De, Neve W. | - |
dc.date.accessioned | 2024-01-09T13:37:03Z | - |
dc.date.available | 2024-01-09T13:37:03Z | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/70300 | - |
dc.description.abstract | Rotator Cuff Tears (RCTs) are a common injury among people who are middle-aged or older. For effective diagnosis of RCTs, orthopedic surgeons typically need to have access to both shoulder Magnetic Resonance Imaging (MRI) and proton density-weighted imaging. However, the generation and interpretation of such comprehensive image information is labor intensive, and thus time consuming and costly. Although computer-aided diagnosis can help in mitigating the aforementioned issues, no computational tools are currently available for diagnosing RCTs. Therefore, we introduce a computational approach towards RCT diagnosis in this paper, leveraging end-to-end learning by applying a deep convolutional neural network to shoulder MRI scans. Given that these shoulder MRI scans are 3-D by nature and highly biased towards normal shoulders, with only 6.6% of the available shoulder MRI scans containing partial-thickness tears, we made use of two tools to enhance our deep convolutional neural network. First, to enable the utilization of sequential information available in the 3-D MRI scans, we integrated a weighted linear combination layer. Second, to mitigate the presence of class imbalance, we adopted weighted cross-entropy loss. That way, we were able to obtain a diagnostic accuracy of 87% and an M-AUC score of 97%, outperforming a baseline of human annotators (diagnostic accuracy of 76% and an M-AUC score of 81%). In addition, we were able to outperform several approaches using conventional machine learning techniques. Finally, to facilitate further research efforts and ease of benchmarking, we make our dataset of 2, 447 shoulder MRI scans publicly available. © 2020 M. Kim, H.-m. Park, J.Y.K.M. Ph.D., S.H.K.M. Ph.D., S.V.H. Ph.D. & W.D.N. Ph.D. | - |
dc.format.extent | 17 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ML Research Press | - |
dc.title | MRI-based Diagnosis of Rotator Cuff Tears using Deep Learning and Weighted Linear Combinations | - |
dc.type | Article | - |
dc.identifier.bibliographicCitation | Proceedings of Machine Learning Research, v.126, pp 292 - 308 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85114795544 | - |
dc.citation.endPage | 308 | - |
dc.citation.startPage | 292 | - |
dc.citation.title | Proceedings of Machine Learning Research | - |
dc.citation.volume | 126 | - |
dc.type.docType | Conference Paper | - |
dc.publisher.location | 영국 | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang 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.