mlsanityData to 7 Oct 2026

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

arXiv 1901.07031 ↗First posted 21 Jan 2019cs.CV cs.AI cs.LGcited by 1,410

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Cited by 182 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. The dataset is freely available at https://stanfordmlgroup.github.io/competitions/chexpert .

Key facts. Cited by 1,410 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2019.

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Citing papers per year: 2021: 160; 2022: 171; 2023: 263; 2024: 279; 2025: 300; 2026: 237.

1,410 papers.

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  2. 2022MedCLIP: Contrastive Learning from Unpaired Medical Images and Textcited by 564
  3. 2023BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairscited by 506
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  11. 2025Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoningcited by 217
  12. 2023Learning to Exploit Temporal Structure for Biomedical Vision-Language Processingcited by 210
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  1. 2022Large Language Models are Few-Shot Clinical Information Extractorscited by 206
  2. 2024Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomographycited by 192
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  4. 2023Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Datacited by 176
  5. 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 162
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  10. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  11. 2023MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiologycited by 149
  12. 2024MAIRA-2: Grounded Radiology Report Generationcited by 140
  13. 2021TorchXRayVision: A library of chest X-ray datasets and modelscited by 131
  14. 2024Exploring scalable medical image encoders beyond text supervisioncited by 129
  15. 2023Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Imagescited by 128
  16. 2024GREEN: Generative Radiology Report Evaluation and Error Notationcited by 124
  17. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  18. 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Reviewcited by 121
  19. 2023ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Modelscited by 116
  20. 2023ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generationcited by 116
  21. 2023Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generationcited by 114
  22. 2023R2GenGPT: Radiology Report Generation with Frozen LLMscited by 111
  23. 2021EnD: Entangling and Disentangling deep representations for bias correctioncited by 105
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  25. 2023EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Modelscited by 101
  26. 2022AUC Maximization in the Era of Big Data and AI: A Surveycited by 100
  27. 2024CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image Formatscited by 100
  28. 2023METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokenscited by 98
  29. 2021Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Trainingcited by 96
  30. 2024CT2Rep: Automated Radiology Report Generation for 3D Medical Imagingcited by 96
  31. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  32. 2022Improving Chest X-Ray Report Generation by Leveraging Warm Startingcited by 93
  33. 2023Generative models improve fairness of medical classifiers under distribution shiftscited by 90
  34. 2021Chest ImaGenome Dataset for Clinical Reasoningcited by 89
  35. 2022RoentGen: Vision-Language Foundation Model for Chest X-ray Generationcited by 89
  36. 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88
  37. 2022Position: Considerations for Differentially Private Learning with Large-Scale Public Pretrainingcited by 86
  38. 2022Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domainscited by 85

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