EFFICIENT BONE SUPPRESSION IN CHEST X-RAY WITH SIMPLIFIED LOSS FUNCTION

Abstract

Chest X-ray imaging is a vital tool for diagnosing thoracic diseases such as pneumonia, lung cancer, and rib fractures. However, rib shadows often obscure or mimic pulmonary lesions - especially in posterior and axillary regions - thereby reducing diagnostic accuracy. Traditional techniques like dual-energy subtraction can separate bone and soft tissue structures but require specialized equipment and may increase radiation exposure. In this paper, we survey representative deep learning architectures applied to the task of bone suppression in standard chest X-ray images, aiming to improve the visibility of lung abnormalities without the need for additional hardware. Our main contribution lies in the use of a simplified loss function that reduces computational complexity while maintaining high inference accuracy. The effectiveness of this loss function is evaluated across various models to demonstrate its performance in suppressing bone shadows.