# Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese

arXiv: https://arxiv.org/abs/2209.04794 · HTML: https://mlsanity.com/paper/2209.04794/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 48 references that are in the map.
- First posted on arXiv 11 Sep 2022.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations](https://mlsanity.com/paper/2012.15029/) (2020)
- [Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets](https://mlsanity.com/paper/2007.09654/) (2020)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [NegBio: a high-performance tool for negation and uncertainty detection in radiology reports](https://mlsanity.com/paper/1712.05898/) (2017)
- [CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation](https://mlsanity.com/paper/2101.06871/) (2021)
- [Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels](https://mlsanity.com/paper/2005.12734/) (2020)
- [VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels](https://mlsanity.com/paper/2102.11467/) (2021)
- [CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output](https://mlsanity.com/paper/2006.15229/) (2020)
- [VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays](https://mlsanity.com/paper/2107.01327/) (2021)
- [VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs](https://mlsanity.com/paper/2106.12930/) (2021)
- [PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children](https://mlsanity.com/paper/2203.10612/) (2022)
- [CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting](https://mlsanity.com/paper/2002.11379/) (2020)
- [A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms](https://mlsanity.com/paper/2112.04490/) (2021)
- [Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis](https://mlsanity.com/paper/2203.10611/) (2022)
- [DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans](https://mlsanity.com/paper/2108.06490/) (2021)
- [ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography](https://mlsanity.com/paper/1807.03058/) (2018)
- [Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning](https://mlsanity.com/paper/2003.14363/) (2020)

## Built on by (most cited first, 5 of 5)

- [Visual RAG: Expanding MLLM visual knowledge without fine-tuning](https://mlsanity.com/paper/2501.10834/) (2025)
- [German CheXpert Chest X-ray Radiology Report Labeler](https://mlsanity.com/paper/2306.02777/) (2023)
- [Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation](https://mlsanity.com/paper/2303.16507/) (2023)
- [Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation](https://mlsanity.com/paper/2304.01220/) (2023)
- [MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classification](https://mlsanity.com/paper/2509.04471/) (2025)

## Abstract (arXiv)

We propose a data collecting and annotation pipeline that extracts information from Vietnamese radiology reports to provide accurate labels for chest X-ray (CXR) images. This can benefit Vietnamese radiologists and clinicians by annotating data that closely match their endemic diagnosis categories which may vary from country to country. To assess the efficacy of the proposed labeling technique, we built a CXR dataset containing 9,752 studies and evaluated our pipeline using a subset of this dataset. With an F1-score of at least 0.9923, the evaluation demonstrates that our labeling tool performs precisely and consistently across all classes. After building the dataset, we train deep learning models that leverage knowledge transferred from large public CXR datasets. We employ a variety of loss functions to overcome the curse of imbalanced multi-label datasets and conduct experiments with various model architectures to select the one that delivers the best performance. Our best model (CheXpert-pretrained EfficientNet-B2) yields an F1-score of 0.6989 (95% CI 0.6740, 0.7240), AUC of 0.7912, sensitivity of 0.7064 and specificity of 0.8760 for the abnormal diagnosis in general. Finally, we demonstrate that our coarse classification (based on five specific locations of abnormalities) yields comparable results to fine classification (twelve pathologies) on the benchmark CheXpert dataset for general anomaly detection while delivering better performance in terms of the average performance of all classes.