{"id":"W2987983801","doi":"10.1088/1361-6560/ab5427","title":"Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth","year":2019,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Princess Margaret Cancer Centre; Institute of Cancer Research; Ontario Institute for Cancer Research; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Terry Fox Research Institute; Ontario Institute for Cancer Research","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Resampling; Image quality; Robustness (evolution); Pattern recognition (psychology); Quality assurance; Grid; Visualization; Artifact (error); Computer vision; Image (mathematics); Mathematics; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001989598,0.0007172074,0.0003818595,0.0006816176,0.0002296444,0.0009153372,0.000571393,0.0005395593,0.0009492306],"category_scores_gemma":[0.00592357,0.0002988857,0.0003625128,0.0003416855,0.0003274457,0.0008889061,0.000637556,0.0006202346,0.0002963219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007464674,"about_ca_system_score_gemma":0.000713059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00592213,"about_ca_topic_score_gemma":0.006706404,"domain_scores_codex":[0.9995133,0.0001001322,0.00004303635,0.0001278991,0.0001271068,0.00008850957],"domain_scores_gemma":[0.9984348,0.0006777251,0.0002737059,0.0001745442,0.0003785262,0.00006067038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001876889,0.0004038902,0.0689433,0.0002900773,0.0003912712,0.0002554248,0.0002242794,0.1844615,0.1970254,0.001102036,0.00332802,0.5416979],"study_design_scores_gemma":[0.00001481046,0.0001779525,0.02111226,0.00003265254,0.00008976526,0.0001234748,0.00005398888,0.9189795,0.05841502,0.0003758552,0.0005985158,0.00002620275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9059579,0.0009528566,0.08886506,0.0003918609,0.00007019859,0.00008776481,0.00023411,0.001493929,0.001946344],"genre_scores_gemma":[0.9695997,0.0001445884,0.02917658,0.00006998844,0.00001081796,0.00002157426,0.0002273479,0.00006550185,0.0006838415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00592213,"threshold_uncertainty_score":0.01177531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05146469855334476,"score_gpt":0.3661720179653029,"score_spread":0.3147073194119582,"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."}}