2019-05-24-gorriz19a.md

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Contributed Papers	Assessing Knee OA Severity with CNN attention-based end-to-end architectures	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}	inproceedings	Proceedings of Machine Learning Research	gorriz19a	0	Assessing Knee OA Severity with CNN attention-based end-to-end architectures	197	214	197-214	197	false	{G\'orriz}, Marc and Antony, Joseph and McGuinness, Kevin and {Gir\'o-i-Nieto}, Xavier and {O'Connor}, {Noel E.}	given family Marc Górriz given family Joseph Antony given family Kevin McGuinness given family Xavier Giró-i-Nieto given family Noel E. O’Connor	2019-05-24		PMLR	Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning	102	inproceedings	date-parts 2019 5 24	http://proceedings.mlr.press/v102/gorriz19a/gorriz19a.pdf