{"url":"/dataset/mimic-cxr","name":"MIMIC-CXR","full_name":"MIMIC-CXR","description_markdown":"**MIMIC-CXR** from Massachusetts Institute of Technology presents 371,920 chest X-rays associated with 227,943 imaging studies from 65,079 patients. The studies were performed at Beth Israel Deaconess Medical Center in Boston, MA.\r\n\r\nSource: [Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification](https://arxiv.org/abs/1909.01940)\r\nImage Source: [https://arxiv.org/abs/1901.07042](https://arxiv.org/abs/1901.07042)","description_withheld":null,"homepage":"https://physionet.org/content/mimic-cxr/2.0.0/","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/mimic-cxr-a-large-publicly-available-database","title":"MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs","first_author":"Alistair E. W. Johnson","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Multi-Label Classification","url":"/task/multi-label-classification","datasets_with_task":"/datasets/task/multi-label-classification"},{"name":"Medical Report Generation","url":"/task/medical-report-generation","datasets_with_task":"/datasets/task/medical-report-generation"},{"name":"Conditional Text-to-Image Synthesis","url":"/task/conditional-text-to-image-synthesis","datasets_with_task":"/datasets/task/conditional-text-to-image-synthesis"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MIMIC-CXR"],"data_loaders":[{"repo":"https://github.com/Abdullahshade/medical","url":"https://github.com/Abdullahshade/medical","frameworks":["tf"]}],"num_papers_in_archive":240,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/conditional-text-to-image-synthesis-on-mimic","task":"Conditional Text-to-Image Synthesis","dataset_variant":"MIMIC-CXR","rows":13,"metrics":["FID (RadDino)"],"first_row_in_archive_order":{"model":"Sana","paper":"/paper/chexgenbench-a-unified-benchmark-for-fidelity","metrics":{"FID (RadDino)":"54.22"},"code_links":[{"title":"Raman1121/CheXGenBench","url":"https://github.com/Raman1121/CheXGenBench"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-report-generation-on-mimic-cxr","task":"Medical Report Generation","dataset_variant":"MIMIC-CXR","rows":2,"metrics":["BLEU-1","BLEU-2","BLEU-3","BLEU-4","CIDEr","Example-F1-14","Example-Precision-14","Example-Recall-14","METEOR","Micro-F1-5","Micro-Precision-5","Micro-Recall-5","ROUGE-L","F1 RadGraph"],"first_row_in_archive_order":{"model":"RGRG","paper":"/paper/interactive-and-explainable-region-guided","metrics":{"BLEU-1":"37.3","BLEU-2":"24.9","BLEU-3":"17.5","BLEU-4":"12.6","CIDEr":"49.5","Example-F1-14":"0.447","Example-Precision-14":"0.461","Example-Recall-14":"0.475","METEOR":"16.8","Micro-F1-5":"0.547","Micro-Precision-5":"0.491","Micro-Recall-5":"0.617","ROUGE-L":"26.4"},"code_links":[{"title":"ttanida/rgrg","url":"https://github.com/ttanida/rgrg"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-label-classification-on-mimic-cxr","task":"Multi-Label Classification","dataset_variant":"MIMIC-CXR","rows":1,"metrics":["Average AUC on 14 label"],"first_row_in_archive_order":{"model":"DensNet121","paper":"/paper/chexclusion-fairness-gaps-in-deep-chest-x-ray","metrics":{"Average AUC on 14 label":"0.8340000000000001"},"code_links":[{"title":"LalehSeyyed/CheXclusion","url":"https://github.com/LalehSeyyed/CheXclusion"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/chexgenbench-a-unified-benchmark-for-fidelity","title":"CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs","date":"2025-05-15","rows_on_this_dataset":13,"code_links":1,"syntology":null},{"paper":"/paper/structural-entities-extraction-and-patient","title":"Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation","date":"2024-05-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/interactive-and-explainable-region-guided","title":"Interactive and Explainable Region-guided Radiology Report Generation","date":"2023-04-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":0,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/chexclusion-fairness-gaps-in-deep-chest-x-ray","title":"CheXclusion: Fairness gaps in deep chest X-ray classifiers","date":"2020-02-14","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":10,"samples_ran":0,"samples_unverified":10,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}