Assessing knee OA severity with CNN attention-based end-to-end architectures
Document typeConference lecture
Rights accessOpen Access
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).
CitationGórriz, M. [et al.]. Assessing knee OA severity with CNN attention-based end-to-end architectures. A: International conference on Medical Imaging with Deep Learning. "International Conference on Medical Imaging with Deep Learning: 8-10 July 2019, London, United Kingdom: proceedings of Machine Learning Research". 2019, p. 197-214.
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