{"id":"W3121590924","doi":"10.3389/fmed.2021.729287","title":"COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning","year":2022,"lang":"en","type":"article","venue":"Frontiers in Medicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Health Sciences; McMaster University; Niagara Health System; University of Waterloo","funders":"Frederick National Laboratory for Cancer Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Artificial intelligence; Leverage (statistics); Deep learning; Cohort; Computer science; Coronavirus disease 2019 (COVID-19); Artificial neural network; Benchmark (surveying); Machine learning; Medical physics; Clickstream; Medicine; Radiology; Geography; The Internet; Cartography; Pathology","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":[0.0009732894,0.000371313,0.001146333,0.0004645621,0.0004173718,0.00001109493,0.0003317392,0.00007756319,0.0004520344],"category_scores_gemma":[0.004893949,0.0003629457,0.0001886312,0.0009533394,0.0005289553,0.0001461544,0.0002084378,0.0009425417,0.00000101947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001386206,"about_ca_system_score_gemma":0.0002909724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00415037,"about_ca_topic_score_gemma":0.0001373102,"domain_scores_codex":[0.9968589,0.0003227,0.0007440498,0.0007753645,0.0007310617,0.000567918],"domain_scores_gemma":[0.9974145,0.001105991,0.0004691396,0.0005214085,0.0001039466,0.0003850468],"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":[0.006090239,0.0009020328,0.115349,0.001645001,0.0007544618,0.001126092,0.02031859,0.4615582,0.02347197,0.00001250785,0.2867438,0.0820282],"study_design_scores_gemma":[0.03209184,0.003835422,0.009864278,0.0004632253,0.001598242,0.0001189687,0.04107153,0.5199651,0.009174065,0.0007675363,0.3800091,0.001040732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2305055,0.008757534,0.7144977,0.03811888,0.00546944,0.00217516,0.0000899075,0.0003268132,0.00005910957],"genre_scores_gemma":[0.9673342,0.0005644402,0.004487952,0.02574927,0.0007246151,0.000378489,0.0004898809,0.00008291502,0.0001883098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7368286,"threshold_uncertainty_score":0.9998822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02623701137377057,"score_gpt":0.3202239473200387,"score_spread":0.2939869359462681,"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."}}