{"id":"W4223955143","doi":"10.1016/j.neunet.2022.03.034","title":"Think positive: An interpretable neural network for image recognition","year":2022,"lang":"en","type":"article","venue":"Neural Networks","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Interpretability; Deep learning; Artificial intelligence; Computer science; Machine learning; Transparency (behavior); Gold standard (test); Coronavirus disease 2019 (COVID-19); Artificial neural network; Pneumonia; Medicine; Disease; Infectious disease (medical specialty); Radiology; Pathology; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004564419,0.0002614692,0.0003885968,0.00008842594,0.0005867605,0.00008894451,0.0002163821,0.00009837071,0.0005173522],"category_scores_gemma":[0.0001077813,0.0002701927,0.0002275088,0.000419959,0.00006852295,0.0003075789,0.000203627,0.0007442234,0.000007548934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002225738,"about_ca_system_score_gemma":0.00005208889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001137634,"about_ca_topic_score_gemma":0.00002613989,"domain_scores_codex":[0.9978464,0.0002488036,0.0003556202,0.0005799389,0.0002987092,0.0006705547],"domain_scores_gemma":[0.9985138,0.0005445189,0.0001490001,0.0004315738,0.0001412454,0.0002198918],"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":[0.004336792,0.0009012446,0.002915505,0.0001334424,0.0001836074,0.0004259361,0.001064051,0.4058357,0.001700584,0.00008644989,0.3993856,0.1830311],"study_design_scores_gemma":[0.001803373,0.002077954,0.0043518,0.00008008778,0.0002457744,0.0002393068,0.0001174308,0.980014,0.00009608217,0.0004386632,0.0101852,0.0003502799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9409065,0.001215894,0.01005376,0.03763013,0.005072156,0.003507894,0.0002169846,0.0009827408,0.0004138912],"genre_scores_gemma":[0.9277105,0.00002211576,0.002135122,0.06631144,0.002171116,0.000279774,0.001004069,0.0001005662,0.0002653305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5741784,"threshold_uncertainty_score":0.999975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257318857426956,"score_gpt":0.3099001337531738,"score_spread":0.2841682480104782,"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."}}