ArticleJournal of digital imaging2022
Effect of Training Data Volume on Performance of Convolutional Neural Network Pneumothorax Classifiers.
Article in Journal of digital imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
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- Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data specific to three disease groups.Scientific reports · 2024Article
- Radiographic chest wall abnormalities in primary spontaneous pneumothorax identified by artificial intelligence.Heliyon · 2024Article
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- Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.Pediatric radiology · 2024Review
- Privacy, Please: Safeguarding Medical Data in Imaging AI Using Differential Privacy Techniques.Radiology. Artificial intelligence · 2024Article
- Effect of Contrast Level and Image Format on a Deep Learning Algorithm for the Detection of Pneumothorax with Chest Radiography.Journal of digital imaging · 2023Article
- Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Large datasets with high-quality labels required to train deep neural networks are challenging to obtain in the radiology domain. This work investigates the effect of training dataset size on the performance of deep learning classifiers, focusing on chest radiograph pneumothorax detection as a proxy visual task in the radiology domain. Two open-source datasets (ChestX-ray14 and CheXpert) comprising 291,454 images were merged and convolutional neural networks trained with stepwise increase in training dataset sizes. Model iterations at each dataset volume were evaluated on an external test set of 525 emergency department chest radiographs. Learning curve analysis was performed to fit the observed AUCs for all models generated. For all three network architectures tested, model AUCs and accuracy increased rapidly from 2 × 10
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Registered trials
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.