Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model

Abstract

. In this work, we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical regions. Compared with the conventional model trained on a single dataset, this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark location.