{"id":"W3040456114","doi":"10.3390/diagnostics10070451","title":"Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening","year":2020,"lang":"en","type":"article","venue":"Diagnostics","topic":"Cervical Cancer and HPV Research","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Cervix; Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Reliability (semiconductor); Cervical cancer; Image quality; Computer vision; Machine learning; Image (mathematics); Cancer; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003508288,0.0001715275,0.000367452,0.00008146561,0.00009304285,0.00005359815,0.000109192,0.0001622435,0.0006255353],"category_scores_gemma":[0.007190436,0.000168433,0.0001110062,0.0005494948,0.00005257062,0.0001146992,0.0001080102,0.000653251,0.00002564777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002261917,"about_ca_system_score_gemma":0.0001605812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003803803,"about_ca_topic_score_gemma":0.0001581102,"domain_scores_codex":[0.9982185,0.000083084,0.0003675075,0.0004677511,0.0003490887,0.0005140032],"domain_scores_gemma":[0.9981592,0.0009153596,0.00006227396,0.0001334073,0.0004120263,0.0003176822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00319767,0.0004502838,0.4477863,0.004301698,0.00007699389,0.00007439439,0.002980266,0.006589467,0.006456736,0.00009013513,0.002931983,0.5250641],"study_design_scores_gemma":[0.002360187,0.0008357695,0.3385189,0.00004707184,0.00006515672,0.000004342658,0.000220946,0.6491111,0.005016516,0.00007380806,0.003554822,0.000191352],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6104313,0.002374352,0.3508027,0.02079828,0.0004463792,0.005795103,0.00005646684,0.002447388,0.006848082],"genre_scores_gemma":[0.9884418,0.0002002031,0.009728376,0.0008199493,0.0004606195,0.0001875801,0.0000752064,0.0000483899,0.00003785056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6425216,"threshold_uncertainty_score":0.8608143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03775542056428126,"score_gpt":0.3390865397242173,"score_spread":0.301331119159936,"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."}}