# Chest X-Ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings arXiv: https://arxiv.org/abs/2104.04518 ยท HTML: https://mlsanity.com/paper/2104.04518/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 11 of its 50 references that are in the map. - First posted on arXiv 9 Apr 2021. ## Builds on (most cited first, 11 of 11) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017) - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016) - [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016) - [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017) - [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019) - [Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation](https://mlsanity.com/paper/1803.01199/) (2018) - [Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1705.09850/) (2017) - [Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer](https://mlsanity.com/paper/1712.07632/) (2017) - [Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods](https://mlsanity.com/paper/2006.00730/) (2020) ## Built on by (most cited first, 13 of 13) - [A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis](https://mlsanity.com/paper/2108.05930/) (2021) - [RepViT-CXR: A Channel Replication Strategy for Vision Transformers in Chest X-ray Tuberculosis and Pneumonia Classification](https://mlsanity.com/paper/2509.08234/) (2025) - [An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detection](https://mlsanity.com/paper/2410.16143/) (2024) - [BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images](https://mlsanity.com/paper/2311.15328/) (2023) - [BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models](https://mlsanity.com/paper/2412.15670/) (2024) - [A bone suppression model ensemble to improve COVID-19 detection in chest X-rays](https://mlsanity.com/paper/2111.03404/) (2021) - [An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis](https://mlsanity.com/paper/2302.09696/) (2023) - [GL-LCM: Global-Local Latent Consistency Models for Fast High-Resolution Bone Suppression in Chest X-Ray Images](https://mlsanity.com/paper/2508.03357/) (2025) - [Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?](https://mlsanity.com/paper/2301.04032/) (2023) - [Semantically Redundant Training Data Removal and Deep Model Classification Performance: A Study with Chest X-rays](https://mlsanity.com/paper/2309.09773/) (2023) - [MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classification](https://mlsanity.com/paper/2506.21199/) (2025) - [X-ray transferable polyrepresentation learning](https://mlsanity.com/paper/2507.06264/) (2025) - [Anatomy-Decomposed Chest Computed Tomography (CT) Projections as Scalable Supervision for Bone Suppression in Chest Radiographs](https://mlsanity.com/paper/2609.24937/) (2026) ## Abstract (arXiv) Chest X-rays are the most commonly performed diagnostic examination to detect cardiopulmonary abnormalities. However, the presence of bony structures such as ribs and clavicles can obscure subtle abnormalities, resulting in diagnostic errors. This study aims to build a deep learning-based bone suppression model that identifies and removes these occluding bony structures in frontal CXRs to assist in reducing errors in radiological interpretation, including DL workflows, related to detecting manifestations consistent with tuberculosis (TB). Several bone suppression models with various deep architectures are trained and optimized using the proposed combined loss function and their performances are evaluated in a cross-institutional test setting. The best-performing model is used to suppress bones in the publicly available Shenzhen and Montgomery TB CXR collections. A VGG-16 model is pretrained on a large collection of publicly available CXRs. The CXR-pretrained model is then fine-tuned individually on the non-bone-suppressed and bone-suppressed CXRs of Shenzhen and Montgomery TB CXR collections to classify them as showing normal lungs or TB manifestations. The performances of these models are compared using several performance metrics, analyzed for statistical significance, and their predictions are qualitatively interpreted through class-selective relevance maps. It is observed that the models trained on bone-suppressed CXRs significantly outperformed (p<0.05) the models trained on the non-bone-suppressed CXRs. Models trained on bone-suppressed CXRs improved detection of TB-consistent findings and resulted in compact clustering of the data points in the feature space signifying that bone suppression improved the model sensitivity toward TB classification.