{"id":"W2093230932","doi":"10.1007/s11548-007-0072-x","title":"Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"AI in cancer detection","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"False positive paradox; Distortion (music); Reduction (mathematics); Computer science; Artificial intelligence; True positive rate; Computer vision; Pattern recognition (psychology); Mathematics; Bandwidth (computing); Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001699999,0.00009108992,0.0002764634,0.001056334,0.00002412197,0.00001899062,0.0002574026,0.0000861437,5.523401e-7],"category_scores_gemma":[0.00006449447,0.00007334343,0.0001152674,0.0004658141,0.0001631145,0.0002827868,0.00002794136,0.0002440985,2.054328e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001240955,"about_ca_system_score_gemma":0.00008565274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000386881,"about_ca_topic_score_gemma":0.0000131086,"domain_scores_codex":[0.9983775,0.0002135622,0.0008520196,0.0001401757,0.0002889701,0.0001277566],"domain_scores_gemma":[0.998385,0.0005654265,0.0007082065,0.0000800434,0.0002291844,0.00003216624],"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":[0.0008077504,0.0003749598,0.007610118,0.00001342647,0.0001274819,0.0001187426,0.001046176,0.010453,0.05782166,0.0001733246,0.00001470386,0.9214386],"study_design_scores_gemma":[0.004272014,0.001236643,0.3331216,0.0004049437,0.00005190226,0.02432413,0.0002490355,0.6133007,0.01911214,0.003480985,0.00005119924,0.0003947943],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5039614,0.0001858024,0.4952829,0.0001244252,0.0004031459,0.00003390103,0.000001718426,0.000002720573,0.000003935284],"genre_scores_gemma":[0.9923941,0.00003571525,0.007416769,0.0000466296,0.00009949019,9.176603e-7,0.000002590853,0.00000320209,5.695022e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9210439,"threshold_uncertainty_score":0.2990858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02230039573754845,"score_gpt":0.2925099227800277,"score_spread":0.2702095270424792,"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."}}