{"id":"W3159019058","doi":"10.1038/s41597-021-00900-3","title":"COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning","year":2021,"lang":"en","type":"article","venue":"Scientific Data","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":204,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre; McGill University Health Centre; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Computed tomography; Artificial intelligence; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Chest radiograph; Computer science; Pneumonia; Medicine; Tomography; Radiology; Machine learning; Medical physics; Radiography; Infectious disease (medical specialty); Pathology; Disease; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002964787,0.0002809815,0.0004998153,0.0006667773,0.0007570706,0.0005829641,0.0007464954,0.00007553247,0.000879719],"category_scores_gemma":[0.004979244,0.0002948231,0.000052214,0.002691974,0.0005575811,0.0003667702,0.002231657,0.0007769881,0.00009156294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002384816,"about_ca_system_score_gemma":0.001129469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001627985,"about_ca_topic_score_gemma":0.003229416,"domain_scores_codex":[0.9958919,0.0003430308,0.0004773385,0.001983162,0.0006819453,0.0006226687],"domain_scores_gemma":[0.9959301,0.0007690183,0.0001670667,0.002126795,0.00009861995,0.000908338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001359589,0.0008058072,0.2259323,0.001396443,0.0001217777,0.002042016,0.0007568129,0.003468555,0.008265587,0.0001065272,0.7329492,0.02401906],"study_design_scores_gemma":[0.001933109,0.00004667562,0.001517817,0.0001169106,0.00009777024,0.0001981226,0.0003033114,0.04822017,0.0004129327,0.00005483657,0.9468126,0.0002857583],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.3245797,0.124897,0.07820524,0.2674153,0.01021564,0.0120296,0.1745121,0.005858352,0.002287145],"genre_scores_gemma":[0.4008922,0.0008687943,0.01085137,0.03487949,0.0002837188,0.00008073311,0.5497575,0.000154884,0.002231355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3752454,"threshold_uncertainty_score":0.9999504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05262219948600481,"score_gpt":0.3532226569827437,"score_spread":0.3006004574967389,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}