  Knee osteoarthritis (OA) is a common degenerate joint disorder that affects a
large population of elderly people worldwide. Accurate radiographic assessment
of knee OA severity plays a critical role in chronic patient management.
Current clinically-adopted knee OA grading systems are observer subjective and
suffer from inter-rater disagreements. In this work, we propose a
computer-aided diagnosis approach to provide more accurate and consistent
assessments of both composite and fine-grained OA grades simultaneously. A
novel semi-supervised learning method is presented to exploit the underlying
coherence in the composite and fine-grained OA grades by learning from
unlabeled data. By representing the grade coherence using the log-probability
of a pre-trained Gaussian Mixture Model, we formulate an incoherence loss to
incorporate unlabeled data in training. The proposed method also describes a
keypoint-based pooling network, where deep image features are pooled from the
disease-targeted keypoints (extracted along the knee joint) to provide more
aligned and pathologically informative feature representations, for accurate OA
grade assessments. The proposed method is comprehensively evaluated on the
public Osteoarthritis Initiative (OAI) data, a multi-center ten-year
observational study on 4,796 subjects. Experimental results demonstrate that
our method leads to significant improvements over previous strong whole
image-based deep classification network baselines (like ResNet-50).

Image and Video Processing;Computer Vision and Pattern Recognition