  Knee osteoarthritis (OA) is one of the most common musculoskeletal disorders
and requires early-stage diagnosis. Nowadays, the deep convolutional neural
networks have achieved greatly in the computer-aided diagnosis field. However,
the construction of the deep learning models usually requires great amounts of
annotated data, which is generally high-cost. In this paper, we propose a novel
approach for knee cartilage defects assessment, including severity
classification and lesion localization. This can be treated as a subtask of
knee OA diagnosis. Particularly, we design a self-ensembling framework, which
is composed of a student network and a teacher network with the same structure.
The student network learns from both labeled data and unlabeled data and the
teacher network averages the student model weights through the training course.
A novel attention loss function is developed to obtain accurate attention
masks. With dual-consistency checking of the attention in the lesion
classification and localization, the two networks can gradually optimize the
attention distribution and improve the performance of each other, whereas the
training relies on partially labeled data only and follows the semi-supervised
manner. Experiments show that the proposed method can significantly improve the
self-ensembling performance in both knee cartilage defects classification and
localization, and also greatly reduce the needs of annotated data.
