ArticleDiagnostics (Basel, Switzerland)2021
Improved Semantic Segmentation of Tuberculosis-Consistent Findings in Chest X-rays Using Augmented Training of Modality-Specific U-Net Models with Weak Localizations.
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 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Weak Localization of Radiographic Manifestations in Pulmonary Tuberculosis from Chest X-ray: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Diagnostic assistance method for RR-TB/MDR-TB patients under treatment based on CNN-LSTM.Scientific reports · 2025Article
- An adaptive convolution neural network model for tuberculosis detection and diagnosis using semantic segmentation.Polish journal of radiology · 2025Article
- Addressing Class Imbalance with Latent Diffusion-based Data Augmentation for Improving Disease Classification in Pediatric Chest X-rays.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024Article
- Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- RNGU-NET: a novel efficient approach in Segmenting Tuberculosis using chest X-Ray images.PeerJ. Computer science · 2024Article
- A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images.iScience · 2023Article
- Usefulness of copper filters in digital chest radiography based on the relationship between effective detective quantum efficiency and deep learning-based segmentation accuracy of the tumor area.Radiological physics and technology · 2023Article
- Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection.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
- Assessing Inter-Annotator Agreement for Medical Image Segmentation.IEEE access : practical innovations, open solutions · 2023Article
- Editorial on Special Issue "Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases".Diagnostics (Basel, Switzerland) · 2022Article
- Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker's Chest X-ray Radiography.Journal of clinical medicine · 2022Article
- A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs.Bioengineering (Basel, Switzerland) · 2022Article
- Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.Computer methods and programs in biomedicine · 2022Article
- A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays.Diagnostics (Basel, Switzerland) · 2022Article
- Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays.Biomedicines · 2022Article
- Automatically measuring the Cobb angle and screening for scoliosis on chest radiograph with a novel artificial intelligence method.American journal of translational research · 2022Article
- VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images.Diagnostics (Basel, Switzerland) · 2021Article
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5 authors.
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Abstract
Deep learning (DL) has drawn tremendous attention for object localization and recognition in both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional hand-crafted feature-based methods. Medical image modality-specific DL models are better at transferring domain knowledge to a relevant target task than those pretrained on stock photography images. This character helps improve model adaptation, generalization, and class-specific region of interest (ROI) localization. In this study, we train chest X-ray (CXR) modality-specific U-Nets and other state-of-the-art U-Net models for semantic segmentation of tuberculosis (TB)-consistent findings. Automated segmentation of such manifestations could help radiologists reduce errors and supplement decision-making while improving patient care and productivity. Our approach uses the publicly available TBX11K CXR dataset with weak TB annotations, typically provided as bounding boxes, to train a set of U-Net models. Next, we improve the results by augmenting the training data with weak localization, postprocessed into an ROI mask, from a DL classifier trained to classify CXRs as showing normal lungs or suspected TB manifestations. Test data are individually derived from the TBX11K CXR training distribution and other cross-institutional collections, including the Shenzhen TB and Montgomery TB CXR datasets. We observe that our augmented training strategy helped the CXR modality-specific U-Net models achieve superior performance with test data derived from the TBX11K CXR training distribution and cross-institutional collections (
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