Azcona, David ORCID: 0000-0003-3693-7906, McGuinness, Kevin ORCID: 0000-0003-1336-6477 and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 (2020) A comparative study of existing and new deep learning methods for detecting knee injuries using the MRNet dataset. In: 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), 19-22 Oct 2020, Valencia, Spain (Online).
Abstract
This work presents a comparative study of existing and new techniques to detect knee injuries by leveraging Stanford's MRNet Dataset. All approaches are based on deep learning and we explore the comparative performances of transfer learning and a deep residual network trained from scratch. We also exploit some characteristics of Magnetic Resonance Imaging (MRI) data by, for example, using a fixed number of slices or 2D images from each of the axial, coronal and sagittal planes as well as combining the three planes into one multi-plane network. Overall we achieved a performance of 93.4% AUC on the validation data by using the more recent deep learning architectures and data augmentation strategies. More flexible architectures are also proposed that might help with the development and training of models that process MRIs. We found that transfer learning and a carefully tuned data augmentation strategy were the crucial factors in determining best performance.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Uncontrolled Keywords: | knee injury; ACL tear; Magnetic Resonance Imaging; MRI; Deep Learning; ACL |
Subjects: | Computer Science > Artificial intelligence Computer Science > Machine learning |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering Research Institutes and Centres > INSIGHT Centre for Data Analytics |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Official URL: | https://dx.doi.org/10.1109/IDSTA50958.2020.9264030 |
Copyright Information: | © IEEE |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | Science Foundation Ireland grant SFI/12/RC/2289 P2 and SFI/15/SIRG/3283 |
ID Code: | 25068 |
Deposited On: | 23 Oct 2020 11:17 by Alan Smeaton . Last Modified 13 May 2021 14:15 |
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