{"id":"W3123505098","doi":"10.15353/jcvis.v6i1.3546","title":"COVID-19 Detection from Chest X-Ray Images Using Deep Convolutional Neural Networks with Weights Imprinting Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada; University of Ottawa","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Convolutional neural network; Deep learning; Artificial intelligence; Computer science; Sensitivity (control systems); Pattern recognition (psychology); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Artificial neural network; Virology; Engineering; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005548951,0.0001971202,0.0004519617,0.0002496508,0.00029203,0.0002535051,0.00006973565,0.00005963753,0.00001874776],"category_scores_gemma":[0.0003268782,0.0001545429,0.0001202297,0.0002848701,0.0001137165,0.0003397684,0.00005003877,0.0003668661,7.453216e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003140018,"about_ca_system_score_gemma":0.0006158745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001499003,"about_ca_topic_score_gemma":0.0000019371,"domain_scores_codex":[0.9979911,0.000217892,0.000652949,0.0002692555,0.0006705054,0.0001983062],"domain_scores_gemma":[0.9972834,0.0007459393,0.0006007086,0.0001154996,0.0008757795,0.0003786536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001923782,0.0001738486,0.01949563,0.0002077162,0.0001533498,0.0002232557,0.0002634725,0.9723539,0.003318967,0.00004548911,0.0003766558,0.003195346],"study_design_scores_gemma":[0.002328833,0.0000889744,0.04570458,0.0004336873,0.0001639572,0.004318736,0.0005618189,0.944918,0.00007392902,0.0001089532,0.001141222,0.0001572744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2346533,0.00286212,0.7590089,0.002833122,0.000460143,0.0001342674,0.000004376889,0.00002869555,0.00001507243],"genre_scores_gemma":[0.9691117,0.00002147323,0.02739532,0.002621556,0.000780531,0.000002137735,0.00003085373,0.00002597103,0.00001045074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7344584,"threshold_uncertainty_score":0.6302078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.020031737229309,"score_gpt":0.3024181420925448,"score_spread":0.2823864048632357,"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."}}