{"id":"W3200852930","doi":"10.3390/diagnostics11091732","title":"Object or Background: An Interpretable Deep Learning Model for COVID-19 Detection from CT-Scan Images","year":2021,"lang":"en","type":"article","venue":"Diagnostics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Deep learning; Coronavirus disease 2019 (COVID-19); Computer science; Transparency (behavior); Process (computing); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Object detection; Pandemic; Looming; 2019-20 coronavirus outbreak; Machine learning; Computer vision; Pattern recognition (psychology); Medicine; Psychology; Computer security; Infectious disease (medical specialty); Virology","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003272292,0.0003097173,0.0005320415,0.0001565815,0.0003127532,0.0002233498,0.0001631135,0.000137426,0.0002836075],"category_scores_gemma":[0.01678898,0.0003058605,0.000178764,0.0003331292,0.00008653531,0.0003295973,0.0001247504,0.0004157963,0.00003397856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007099301,"about_ca_system_score_gemma":0.001041759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00127801,"about_ca_topic_score_gemma":0.003201364,"domain_scores_codex":[0.9978675,0.0001443117,0.000422011,0.0007448517,0.0003309619,0.0004903669],"domain_scores_gemma":[0.9929347,0.005461193,0.0001539564,0.0006057436,0.0003321281,0.0005123038],"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":[0.004324297,0.005758934,0.08929383,0.003316995,0.001598261,0.004871667,0.01731304,0.5020354,0.06495287,0.0004355684,0.06668133,0.2394178],"study_design_scores_gemma":[0.004248133,0.0008934797,0.003630675,0.0004036676,0.00100311,0.0001392505,0.002210109,0.8757327,0.03801224,0.001126683,0.07189332,0.0007066946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2965616,0.001268872,0.6954508,0.004551252,0.0006466547,0.0007693123,0.0002034679,0.0004648147,0.00008320811],"genre_scores_gemma":[0.9541698,0.001192776,0.01737487,0.02442061,0.0005144375,0.000260825,0.0006311291,0.000131182,0.001304343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6780759,"threshold_uncertainty_score":0.9999393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05510483697287936,"score_gpt":0.3586338732976522,"score_spread":0.3035290363247728,"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."}}