  Tendon injuries like tendinopathies, full and partial thickness tears are
prevalent, and the supraspinatus tendon (SST) is the most vulnerable ones in
the rotator cuff. Early diagnosis of SST tendinopathies is of high importance
and hard to achieve using ultrasound imaging. In this paper, an automatic
tendinopathy recognition framework based on convolutional neural networks has
been proposed to assist the diagnosis. This framework has two essential parts
of tendon segmentation and classification. Tendon segmentation is done through
a novel network, NASUNet, which follows an encoder-decoder architecture
paradigm and utilizes a multi-scale Enlarging cell. Moreover, a general
classification pipeline has been proposed for tendinopathy recognition, which
supports different base models as the feature extractor engine. Two feature
maps comprising positional information of the tendon region have been
introduced as the network input to make the classification network
spatial-aware. To evaluate the tendinopathy recognition system, a data set
consisting of 100 SST ultrasound images have been acquired, in which
tendinopathy cases are double-verified by magnetic resonance imaging. In both
segmentation and classification tasks, lack of training data has been
compensated by incorporating knowledge transferring, transfer learning, and
data augmentation techniques. In cross-validation experiments, the proposed
tendinopathy recognition model achieves 91% accuracy, 86.67% sensitivity, and
92.86% specificity, showing state-of-the-art performance against other models.
