Abstract

This is a end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which uses 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. The attention mechanism is applied on existing state-of-the-art VGG16 architecture as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative.

Documentation

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