A multi-stage semi-supervised learning for ankle fracture classification
  on CT images
Hongzhi Liu Guicheng Li Jiacheng Nie Hui Tang Chunfeng Yang Qianjin Feng Hailin Xu Yang Chen
Because of the complicated mechanism of ankle injury, it is very difficult to
diagnose ankle fracture in clinic. In order to simplify the process of fracture
diagnosis, an automatic diagnosis model of ankle fracture was proposed.
Firstly, a tibia-fibula segmentation network is proposed for the joint
tibiofibular region of the ankle joint, and the corresponding segmentation
dataset is established on the basis of fracture data. Secondly, the image
registration method is used to register the bone segmentation mask with the
normal bone mask. Finally, a semi-supervised classifier is constructed to make
full use of a large number of unlabeled data to classify ankle fractures.
Experiments show that the proposed method can segment fractures with fracture
lines accurately and has better performance than the general method. At the
same time, this method is superior to classification network in several
indexes.