# PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children

arXiv: https://arxiv.org/abs/2203.10612 · HTML: https://mlsanity.com/paper/2203.10612/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [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)
- [Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels](https://mlsanity.com/paper/2005.12734/) (2020)
- [COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images](https://mlsanity.com/paper/2006.01409/) (2020)
- [CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting](https://mlsanity.com/paper/2002.11379/) (2020)
- [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)
- [Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.06486/) (2021)

## Built on by (most cited first, 19 of 19)

- [Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data](https://mlsanity.com/paper/2308.02463/) (2023)
- [Large-scale Long-tailed Disease Diagnosis on Radiology Images](https://mlsanity.com/paper/2312.16151/) (2023)
- [An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph](https://mlsanity.com/paper/2208.03545/) (2022)
- [K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imaging](https://mlsanity.com/paper/2302.11557/) (2023)
- [An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detection](https://mlsanity.com/paper/2410.16143/) (2024)
- [Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations](https://mlsanity.com/paper/2504.08584/) (2025)
- [Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese](https://mlsanity.com/paper/2209.04794/) (2022)
- [Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection](https://mlsanity.com/paper/2508.00438/) (2025)
- [DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training](https://mlsanity.com/paper/2407.11594/) (2024)
- [Resolution scaling governs DINOv3 transfer performance in chest radiograph classification](https://mlsanity.com/paper/2510.07191/) (2025)
- [A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling](https://mlsanity.com/paper/2602.22843/) (2026)
- [MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging](https://mlsanity.com/paper/2604.13756/) (2026)
- [Improving Pediatric Pneumonia Diagnosis with Adult Chest X-ray Images Utilizing Contrastive Learning and Embedding Similarity](https://mlsanity.com/paper/2404.12958/) (2024)
- [Self-Supervised Learning for Building Robust Pediatric Chest X-ray Classification Models](https://mlsanity.com/paper/2409.00231/) (2024)
- [ViX-Ray: A Vietnamese Chest X-Ray Dataset for Vision-Language Models](https://mlsanity.com/paper/2603.15513/) (2026)
- [On-Device Continual Learning with Dual-Stage Buffer and Dynamic Loss for Point-of-Care Pneumonia Diagnosis](https://mlsanity.com/paper/2605.19201/) (2026)
- [CANDOR: Chance-Calibrated Neighborhood Discordance in Frozen Encoders for Medical Imaging](https://mlsanity.com/paper/2607.18451/) (2026)
- [FRAME: Separating sampling variation from performance disparities in medical image analysis](https://mlsanity.com/paper/2608.25981/) (2026)
- [Cross-dataset transportability of pediatric chest X-ray deep learning across three countries: discrimination, calibration, operating-point failure, and limited-label recovery](https://mlsanity.com/paper/2609.05140/) (2026)

## Abstract (arXiv)

The development of diagnostic models for detecting and diagnosing pediatric diseases in CXR scans is undertaken due to the lack of high-quality physician-annotated datasets. To overcome this challenge, we introduce and release PediCXR, a new pediatric CXR dataset of 9,125 studies retrospectively collected from a major pediatric hospital in Vietnam between 2020 and 2021. Each scan was manually annotated by a pediatric radiologist with more than ten years of experience. The dataset was labeled for the presence of 36 critical findings and 15 diseases. In particular, each abnormal finding was identified via a rectangle bounding box on the image. To the best of our knowledge, this is the first and largest pediatric CXR dataset containing lesion-level annotations and image-level labels for the detection of multiple findings and diseases. For algorithm development, the dataset was divided into a training set of 7,728 and a test set of 1,397. To encourage new advances in pediatric CXR interpretation using data-driven approaches, we provide a detailed description of the PediCXR data sample and make the dataset publicly available on https://physionet.org/content/pedicxr/1.0.0/