Assessing Knee OA Severity with CNN attention-based end-to-end architectures

Marc Górriz, Joseph Antony, Kevin McGuinness, Xavier Giró-i-Nieto, Noel E. O’Connor
Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR 102:197-214, 2019.

Abstract

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All code from our experiments will be publicly available on the github repository: \url{https://github.com/marc-gorriz/KneeOA-CNNAttention}

Cite this Paper

BibTeX
@InProceedings{pmlr-v102-gorriz19a, title = {Assessing Knee OA Severity with CNN attention-based end-to-end architectures}, author = {{G\'orriz}, Marc and Antony, Joseph and McGuinness, Kevin and {Gir\'o-i-Nieto}, Xavier and {O'Connor}, {Noel E.}}, booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning}, pages = {197--214}, year = {2019}, editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}, volume = {102}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v102/gorriz19a/gorriz19a.pdf}, url = {https://proceedings.mlr.press/v102/gorriz19a.html}, abstract = {This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All code from our experiments will be publicly available on the github repository: \url{https://github.com/marc-gorriz/KneeOA-CNNAttention}} }
Endnote
%0 Conference Paper %T Assessing Knee OA Severity with CNN attention-based end-to-end architectures %A Marc Górriz %A Joseph Antony %A Kevin McGuinness %A Xavier Giró-i-Nieto %A Noel E. O’Connor %B Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2019 %E M. Jorge Cardoso %E Aasa Feragen %E Ben Glocker %E Ender Konukoglu %E Ipek Oguz %E Gozde Unal %E Tom Vercauteren %F pmlr-v102-gorriz19a %I PMLR %P 197--214 %U https://proceedings.mlr.press/v102/gorriz19a.html %V 102 %X This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All code from our experiments will be publicly available on the github repository: \url{https://github.com/marc-gorriz/KneeOA-CNNAttention}
APA
Górriz, M., Antony, J., McGuinness, K., Giró-i-Nieto, X. & O’Connor, N.E.. (2019). Assessing Knee OA Severity with CNN attention-based end-to-end architectures. Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 102:197-214 Available from https://proceedings.mlr.press/v102/gorriz19a.html.

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