{"id":"W2027162949","doi":"10.1118/1.2988161","title":"Comparison of model and human observer performance for detection and discrimination tasks using dual‐energy x‐ray images","year":2008,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto","funders":"National Cancer Institute; National Institutes of Health; University of Toronto; Carestream Health","keywords":"Observer (physics); Artificial intelligence; Smoothing; Computer science; Background subtraction; Mathematics; Energy (signal processing); Noise (video); Computer vision; Subtraction; Algorithm; Pattern recognition (psychology); Pixel; Image (mathematics); Physics; Statistics","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":[0.004201406,0.0005091478,0.0005045511,0.0003075322,0.0002620566,0.0009485783,0.0004635082,0.0005897687,0.0009208127],"category_scores_gemma":[0.01393351,0.0002551569,0.0005456725,0.0001772122,0.0004558635,0.0007944483,0.0007093692,0.0003619069,0.0004817576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006004105,"about_ca_system_score_gemma":0.0004507568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002694279,"about_ca_topic_score_gemma":0.001697833,"domain_scores_codex":[0.9977658,0.0009961309,0.0001010185,0.0007085657,0.0003063353,0.0001223001],"domain_scores_gemma":[0.9925403,0.005042932,0.0003963672,0.0009333135,0.0009608407,0.0001262139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00753765,0.001043142,0.1340846,0.0009835014,0.001211518,0.000418584,0.005991118,0.2383514,0.4113605,0.006142298,0.002319352,0.1905563],"study_design_scores_gemma":[0.0001129066,0.002512682,0.08210684,0.0000345663,0.0001841681,0.0004629551,0.000401943,0.8408176,0.06969774,0.002357243,0.001163309,0.000148101],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7958447,0.0001231327,0.2022819,0.00004131528,0.00002275927,0.00005632043,0.000114863,0.0003731833,0.001141813],"genre_scores_gemma":[0.9803903,0.00004738461,0.01891494,0.00001599427,0.00000344744,0.00004588769,0.00016796,0.00004953604,0.0003646405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004201406,"threshold_uncertainty_score":0.02221948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04780491761681299,"score_gpt":0.2994047153244425,"score_spread":0.2515997977076295,"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."}}