{"url":"/dataset/covidgr","name":"COVIDGR","full_name":null,"description_markdown":"Under a close collaboration with an expert radiologist team of the Hospital Universitario San Cecilio, the **COVIDGR**-1.0 dataset of patients' anonymized X-ray images has been built. 852 images have been collected following a strict labeling protocol. They are categorized into 426 positive cases and 426 negative cases. Positive images correspond to patients who have been tested positive for COVID-19 using RT-PCR within a time span of at most 24h between the X-ray image and the test. Every image has been taken using the same type of equipment and with the same format: only the posterior-anterior view is considered.\n\nSource: [https://github.com/ari-dasci/covidgr](https://github.com/ari-dasci/covidgr)","description_withheld":null,"homepage":"https://github.com/ari-dasci/covidgr","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/covidgr-dataset-and-covid-sdnet-methodology","title":"COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images","first_author":"S. Tabik","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"Medical Image Classification","url":"/task/medical-image-classification","datasets_with_task":"/datasets/task/medical-image-classification"},{"name":"COVID-19 Diagnosis","url":"/task/covid-19-detection","datasets_with_task":"/datasets/task/covid-19-detection"}],"languages":[],"variants":["COVIDGR"],"data_loaders":[{"repo":"https://github.com/ari-dasci/covidgr","url":"https://github.com/ari-dasci/covidgr","frameworks":[]}],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/covid-19-diagnosis-on-covidgr","task":"COVID-19 Diagnosis","dataset_variant":"COVIDGR","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DINO-CXR","paper":"/paper/dino-cxr-a-self-supervised-method-based-on","metrics":{"Accuracy":"76.47"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-classification-on-covidgr","task":"Medical Image Classification","dataset_variant":"COVIDGR","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DINO-CXR","paper":"/paper/dino-cxr-a-self-supervised-method-based-on","metrics":{"Accuracy":"76.47"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dino-cxr-a-self-supervised-method-based-on","title":"DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification","date":"2023-08-01","rows_on_this_dataset":2,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}