ArticleScientific data2023
PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 39 citations in OpenAlex.
- Resolution-dependent self-supervised transfer in chest radiograph classification.Communications medicine · 2026Article
- Bone age estimation from chest radiographs using deep neural networks: a proof-of-concept study.Pediatric radiology · 2026Article
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Performance of adult-trained artificial intelligence models in paediatric imaging-a scoping review.European radiology · 2026Article
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- Evidence-Guided Diagnostic Reasoning for Pediatric Chest Radiology Based on Multimodal Large Language Models.Journal of imaging · 2026Article
- Bridging the gap: the need for pediatric-specific AI models for tuberculosis detection on chest radiographs.Annals of medicine and surgery (2012) · 2026Article
- A Comprehensive X-ray Dataset for Pediatric Ulna and Radius Fractures Analysis.Scientific data · 2026Article
- PLUTO: a YOLO-based lung field detector for pediatric lateral chest X-rays generalizable to adults.Frontiers in artificial intelligence · 2026Article
- Lack of children in public medical imaging data points to growing age bias in biomedical AI.Research square · 2025Article
- Integrating snapshot ensemble learning into masked autoencoders for efficient self-supervised pretraining in medical imaging.Scientific reports · 2025Article
- Foundation models for radiology-the position of the AI for Health Imaging (AI4HI) network.Insights into imaging · 2025Review
- Efficient labeling for fine-tuning chest X-ray bone-suppression networks for pediatric patients.Medical physics · 2025Article
- CurriMAE: curriculum learning based masked autoencoders for multi-labeled pediatric thoracic disease classification.PeerJ. Computer science · 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
- Large-scale long-tailed disease diagnosis on radiology images.Nature communications · 2024Article
- Tiny Lungs, Big Challenges: Pediatric and Premature Lung Segmentation using Deep Learning.... International Symposium on Medical Information Processing and Analysis. International SIPAIM Workshop · 2024Article
- Self-supervised learning framework application for medical image analysis: a review and summary.Biomedical engineering online · 2024Review
- Three-Stage Framework for Accurate Pediatric Chest X-ray Diagnosis Using Self-Supervision and Transfer Learning on Small Datasets.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and the advent of high-performance supervised learning algorithms. However, 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/vindr-pcxr/1.0.0/ .
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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.