  This paper introduces a new approach to automatically quantify the severity
of knee OA using X-ray images. Automatically quantifying knee OA severity
involves two steps: first, automatically localizing the knee joints; next,
classifying the localized knee joint images. We introduce a new approach to
automatically detect the knee joints using a fully convolutional neural network
(FCN). We train convolutional neural networks (CNN) from scratch to
automatically quantify the knee OA severity optimizing a weighted ratio of two
loss functions: categorical cross-entropy and mean-squared loss. This joint
training further improves the overall quantification of knee OA severity, with
the added benefit of naturally producing simultaneous multi-class
classification and regression outputs. Two public datasets are used to evaluate
our approach, the Osteoarthritis Initiative (OAI) and the Multicenter
Osteoarthritis Study (MOST), with extremely promising results that outperform
existing approaches.
