{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006691719,0.002004988,0.000856475,0.002050872,0.0008220542,0.00116109,0.002479876,0.001696672,0.005928984],"category_scores_gemma":[0.002321102,0.0003986155,0.001130972,0.001953211,0.000529594,0.0007102875,0.001302784,0.001613236,0.005010952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247169,"about_ca_system_score_gemma":0.002275128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02581058,"about_ca_topic_score_gemma":0.05090652,"domain_scores_codex":[0.9993119,0.00009066243,0.00008186662,0.0002100215,0.00019286,0.0001127107],"domain_scores_gemma":[0.9994416,0.00009313969,0.00006570217,0.000121638,0.0001878139,0.00009017609],"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":[0.001102552,0.0007643662,0.03172917,0.001788229,0.0003320743,0.001327848,0.0001116911,0.01425995,0.008907855,0.002190497,0.8722036,0.06528223],"study_design_scores_gemma":[0.001288185,0.0008734085,0.0878107,0.0009256286,0.0004331131,0.006124249,0.0007295772,0.1490631,0.02609253,0.008961991,0.7173226,0.000374971],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04996346,0.002486109,0.01087083,0.001396989,0.0006656171,0.0006882697,0.9218351,0.005563253,0.006530423],"genre_scores_gemma":[0.02947902,0.0004368362,0.009516599,0.0002609868,0.00006611504,0.0003292688,0.958207,0.0001228862,0.001581321],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02581058,"threshold_uncertainty_score":0.05132067,"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."}}