  The thighbone is the largest bone supporting the lower body. If the thighbone
fracture is not treated in time, it will lead to lifelong inability to walk.
Correct diagnosis of thighbone disease is very important in orthopedic
medicine. Deep learning is promoting the development of fracture detection
technology. However, the existing computer aided diagnosis (CAD) methods baesd
on deep learning rely on a large number of manually labeled data, and labeling
these data costs a lot of time and energy. Therefore, we develop a object
detection method with limited labeled image quantity and apply it to the
thighbone fracture localization. In this work, we build a semi-supervised
object detection(SSOD) framework based on single-stage detector, which
including three modules: adaptive difficult sample oriented (ADSO) module,
Fusion Box and deformable expand encoder (Dex encoder). ADSO module takes the
classification score as the label reliability evaluation criterion by
weighting, Fusion Box is designed to merge similar pseudo boxes into a reliable
box for box regression and Dex encoder is proposed to enhance the adaptability
of image augmentation. The experiment is conducted on the thighbone fracture
dataset, which includes 3484 training thigh fracture images and 358 testing
thigh fracture images. The experimental results show that the proposed method
achieves the state-of-the-art AP in thighbone fracture detection at different
labeled data rates, i.e. 1%, 5% and 10%. Besides, we use full data to achieve
knowledge distillation, our method achieves 86.2% AP50 and 52.6% AP75.
