  Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA
diagnosis is currently conducted by assessing symptoms and evaluating plain
radiographs, but this process suffers from subjectivity. In this study, we
present a new transparent computer-aided diagnosis method based on the Deep
Siamese Convolutional Neural Network to automatically score knee OA severity
according to the Kellgren-Lawrence grading scale. We trained our method using
the data solely from the Multicenter Osteoarthritis Study and validated it on
randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative
dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average
multiclass accuracy of 66.71\% compared to the annotations given by a committee
of clinical experts. Here, we also report a radiological OA diagnosis area
under the ROC curve of 0.93. We also present attention maps -- given as a class
probability distribution -- highlighting the radiological features affecting
the network decision. This information makes the decision process transparent
for the practitioner, which builds better trust toward automatic methods. We
believe that our model is useful for clinical decision making and for OA
research; therefore, we openly release our training codes and the data set
created in this study.
