{"id":"W1986753552","doi":"10.1118/1.4865175","title":"Method of measuring NEQ as a quality control metric for digital mammography","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Imaging phantom; Mammography; Image quality; Digital mammography; Optical transfer function; Noise (video); Reproducibility; Quality assurance; Computer science; Medical physics; Optics; Computer vision; Mathematics; Physics; Medicine; Image (mathematics); Statistics","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":[0.00866957,0.001219935,0.0008665105,0.002541036,0.0006191845,0.001326712,0.001821589,0.001244071,0.001689105],"category_scores_gemma":[0.02572024,0.000694018,0.0005692401,0.002093023,0.001328351,0.001061224,0.001203865,0.0010288,0.000703847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001519196,"about_ca_system_score_gemma":0.00123275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001370667,"about_ca_topic_score_gemma":0.00193,"domain_scores_codex":[0.9882798,0.002711103,0.000750763,0.001464355,0.006679151,0.0001147315],"domain_scores_gemma":[0.9845521,0.005536822,0.0020361,0.002385733,0.005334898,0.0001542873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008398172,0.0004497188,0.03564408,0.001812299,0.0003229109,0.0001600223,0.0006019753,0.005630993,0.7364357,0.00393743,0.001951885,0.2122132],"study_design_scores_gemma":[0.000149277,0.001897224,0.07106355,0.0003013621,0.0003496278,0.001952066,0.0001782379,0.05480205,0.8464152,0.002299886,0.02021048,0.0003812191],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04390088,0.002059559,0.9493166,0.0001714612,0.0001641312,0.0009484875,0.0004548513,0.001124775,0.001859289],"genre_scores_gemma":[0.2111609,0.000968794,0.7833195,0.0002212932,0.00004508746,0.002253592,0.0004798682,0.000235919,0.001315084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00866957,"threshold_uncertainty_score":0.04584962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357030306406757,"score_gpt":0.3220193219710157,"score_spread":0.2984490189069481,"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."}}