Antony, Joseph ORCID: 0000-0001-6493-7829, McGuinness, Kevin ORCID: 0000-0003-1336-6477, Moran, Kieran ORCID: 0000-0003-2015-8967 and O'Connor, Noel E. ORCID: 0000-0002-4033-9135 (2017) Automatic detection of joints and quantification of knee osteoarthritis severity using convolutional neural networks. In: 13th International Conference on Machine Learning and Data Mining, 15-20 July 2017, New York, USA.. ISBN 978-3-319-62416-7
Abstract
This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, automatically localizing the knee joints; next, classifying the localized knee joint images. We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN). We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss. This joint training further improves the overall quantification of knee OA severity, with the added benefit of naturally producing simultaneous multi-class classification and regression outputs. Two public datasets are used to evaluate our approach, the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST), with extremely promising results that outperform existing approaches.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Uncontrolled Keywords: | Knee Osteoarthritis; KL grades; Automatic Detection; Fully Convolutional Neural Networks; Classification; Regression |
Subjects: | UNSPECIFIED |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance Research Institutes and Centres > INSIGHT Centre for Data Analytics |
Published in: | 13th International Conference on Machine Learning and Data Mining, Proceedings. Lecture Notes in Computer Science 10358. Springer International Publishing. ISBN 978-3-319-62416-7 |
Publisher: | Springer International Publishing |
Official URL: | https://dx.doi.org/10.1007/978-3-319-62416-7_27 |
Copyright Information: | © 2017 Springer. The original publication is available at www.springerlink.com |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
ID Code: | 21761 |
Deposited On: | 17 Jul 2017 08:42 by Joseph Antony . Last Modified 12 Aug 2020 13:21 |
Documents
Full text available as:
Preview |
PDF
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB |
Metrics
Altmetric Badge
Dimensions Badge
Downloads
Downloads
Downloads per month over past year
Archive Staff Only: edit this record