AN END-TO-END NETWORK FOR DETECTING MULTI-DOMAIN FRACTURES ON X-RAY IMAGES

Automated fracture detection on medical images is a crucial prerequisite for orthopedic diagnosis. However, due to the considerable variation of bone structures, it is challenging to detect fractures on images filmed from various body parts utilizing a single model. In this paper, we treat each body part as a domain and propose a novel Multi-domain Fracture Detection Network (MFDN), which is composed of two sub-networks, namely, a domain classification network for predicting the domain type of an image and a fracture detection network for detecting fractures on X-ray images of different domains. By constructing Feature Enhancement Modules and Muti-Feature-Enhanced R-CNN, the proposed MFDN extracts better feature representations for each domain. Experimental results on real-world datasets show the effectiveness of our model which has been used in clinical diagnosis with the best performance on all the domains.