ArticleDiagnostics (Basel, Switzerland)2021
Chest X-ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.Journal of medical systems · 2022Pooled it
- GAN-based bone suppression using a combined loss function.Frontiers in artificial intelligence · 2026Article
- Lung Segmentation with Lightweight Convolutional Attention Residual U-Net.Diagnostics (Basel, Switzerland) · 2025Article
- Efficient labeling for fine-tuning chest X-ray bone-suppression networks for pediatric patients.Medical physics · 2025Article
- The two-stage detection-after-segmentation model improves the accuracy of identifying subdiaphragmatic lesions.Scientific reports · 2024Article
- Adapted generative latent diffusion models for accurate pathological analysis in chest X-ray images.Medical & biological engineering & computing · 2024Article
- Semantically redundant training data removal and deep model classification performance: A study with chest X-rays.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Self-Trained Convolutional Neural Network (CNN) for Tuberculosis Diagnosis in Medical Imaging.Cureus · 2024Article
- Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- YOLOv8's advancements in tuberculosis identification from chest images.Frontiers in big data · 2024Article
- Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approach.Scientific reports · 2023Article
- Article
- An Efficient and Robust Method for Chest X-ray Rib Suppression That Improves Pulmonary Abnormality Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Assessing the Impact of Image Resolution on Deep Learning for TB Lesion Segmentation on Frontal Chest X-rays.Diagnostics (Basel, Switzerland) · 2023Article
- Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.Quantitative imaging in medicine and surgery · 2023Article
- Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2022Review
- Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.Quantitative imaging in medicine and surgery · 2022Article
- DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs.PloS one · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Chest X-rays (CXRs) 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 (DL)-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 using several metrics such as mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and multiscale structural similarity measure (MS-SSIM). The best-performing model (ResNet-BS) (PSNR = 34.0678; MS-SSIM = 0.9828) 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 such as accuracy, the area under the curve (AUC), sensitivity, specificity, precision, F-score, and Matthews correlation coefficient (MCC), analyzed for statistical significance, and their predictions are qualitatively interpreted through class-selective relevance maps (CRMs). It is observed that the models trained on bone-suppressed CXRs (Shenzhen: AUC = 0.9535 ± 0.0186; Montgomery: AUC = 0.9635 ± 0.0106) significantly outperformed (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.