{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001411703,0.0007783852,0.001019021,0.001130684,0.0003270105,0.0005853656,0.001136331,0.0009871846,0.0008026448],"category_scores_gemma":[0.004817316,0.0003238651,0.000666185,0.0004915684,0.0002679859,0.0007197154,0.0009290245,0.0008950586,0.000405912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005985005,"about_ca_system_score_gemma":0.001009698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007312314,"about_ca_topic_score_gemma":0.009836254,"domain_scores_codex":[0.9993964,0.0001235895,0.0000327355,0.0002005777,0.0001450277,0.0001016107],"domain_scores_gemma":[0.9988428,0.0004393805,0.000110489,0.0001397896,0.0003985541,0.00006888101],"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":[0.0005540572,0.0002479282,0.03334555,0.000124104,0.00023445,0.0002914939,0.0001583357,0.1479467,0.03052325,0.001173906,0.006739998,0.7786602],"study_design_scores_gemma":[0.00001313607,0.000112813,0.00471384,0.0000217153,0.00006158402,0.0001436316,0.00003013155,0.9816092,0.01124984,0.0008880539,0.001140771,0.0000152728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3862419,0.002714773,0.600506,0.00084494,0.0001640376,0.0001695965,0.0007488602,0.005601779,0.003008128],"genre_scores_gemma":[0.8809118,0.000456216,0.1139591,0.0004420485,0.00008975556,0.00008973202,0.00115733,0.0001524889,0.002741545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007312314,"threshold_uncertainty_score":0.01453954,"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."}}