{"id":"W3201066509","doi":"10.3390/covid1010034","title":"Detecting Coronavirus from Chest X-rays Using Transfer Learning","year":2021,"lang":"en","type":"article","venue":"COVID","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Deep learning; Artificial intelligence; Coronavirus disease 2019 (COVID-19); Transfer of learning; Radiography; Computer science; Set (abstract data type); Convolutional neural network; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Machine learning; Pattern recognition (psychology); Medicine; Radiology; Disease; Pathology","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002084459,0.0001839195,0.0003461463,0.00007545339,0.0002281795,0.00005805421,0.00008082225,0.0001345257,0.0008996306],"category_scores_gemma":[0.001240922,0.0002009216,0.0001437564,0.0003145793,0.0000557496,0.00008361344,0.00005914268,0.0004806228,0.00009876853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003439144,"about_ca_system_score_gemma":0.0005624383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008021895,"about_ca_topic_score_gemma":0.0001334557,"domain_scores_codex":[0.9985287,0.0001107758,0.0002683534,0.0004773968,0.0002885798,0.0003261726],"domain_scores_gemma":[0.9986343,0.0006548222,0.00003821248,0.000345071,0.0001181676,0.0002094614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001396231,0.0002372153,0.1164419,0.0002520398,0.0001655277,0.001422299,0.002540046,0.003829296,0.8362969,0.00002274092,0.000229391,0.03842312],"study_design_scores_gemma":[0.005341717,0.0002630614,0.07883085,0.001253234,0.0008929868,0.0002476168,0.001421114,0.01519235,0.4283164,0.0001300529,0.4672887,0.0008219993],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859124,0.001115845,0.008611607,0.003440586,0.0003282356,0.0001465013,0.00000878171,0.0002331631,0.0002028732],"genre_scores_gemma":[0.9842835,0.00005603179,0.002656311,0.01230113,0.0004112032,0.00000851328,0.00003781782,0.00006018197,0.0001853073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4670593,"threshold_uncertainty_score":0.9850324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09739768906885071,"score_gpt":0.3547724696587365,"score_spread":0.2573747805898858,"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."}}