  The accurate localization of inserted medical tubes and parts of human
anatomy is a common problem when analyzing chest radiographs and something deep
neural networks could potentially automate. However, many foreign objects like
tubes and various anatomical structures are small in comparison to the entire
chest X-ray, which leads to severely unbalanced data and makes training deep
neural networks difficult. In this paper, we present a simple yet effective
`Only-One-Object-Exists' (OOOE) assumption to improve the deep network's
ability to localize small landmarks in chest radiographs. The OOOE enables us
to recast the localization problem as a classification problem and we can
replace commonly used continuous regression techniques with a multi-class
discrete objective. We validate our approach using a large scale proprietary
dataset of over 100K radiographs as well as publicly available RANZCR-CLiP
Kaggle Challenge dataset and show that our method consistently outperforms
commonly used regression-based detection models as well as commonly used
pixel-wise classification methods. Additionally, we find that the method using
the OOOE assumption generalizes to multiple detection problems in chest X-rays
and the resulting model shows state-of-the-art performance on detecting various
tube tips inserted to the patient as well as patient anatomy.
