Abstract
Chest radiography is one of the most ubiquitous medical imaging exams used for the diagnosis and follow-up of a wide array of pathologies. However, chest radiography analysis is time consuming and often challenging, even for experts. This has led to the development of numerous automatic solutions for multipathology detection in chest radiography, particularly after the advent of deep learning. However, the black-box nature of deep learning solutions together with the inherent class imbalance of medical imaging problems often leads to weak generalization capabilities, with models learning features based on spurious correlations such as the aspect and position of laterality, patient position, equipment and hospital markers. In this study, an automatic method based on a YOLOv3 framework was thus developed for the detection of markers and written labels in chest radiography images. It is shown that this model successfully detects a large proportion of markers in chest radiography, even in datasets different from the training source, with a low rate of false positives per image. As such, this method could be used for performing automatic obscuration of markers in large datasets, so that more generic and meaningful features can be learned, thus improving classification performance and robustness.