{"id":"W2025845047","doi":"10.4018/jcini.2009070103","title":"Classification of Breast Masses in Mammograms Using Radial Basis Functions and Simulated Annealing","year":2009,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"AI in cancer detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Radial basis function; Computer science; Pattern recognition (psychology); Simulated annealing; Artificial intelligence; Classifier (UML); Artificial neural network; Mammography; Receiver operating characteristic; Breast cancer; Algorithm; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002936648,0.00009001146,0.0001420185,0.0004142408,0.00003909585,0.0001197791,0.0002341488,0.00004952219,0.000003740417],"category_scores_gemma":[0.0001407065,0.00007933737,0.00004249067,0.0002880822,0.00007067595,0.001290566,0.00004578379,0.0002252033,4.767922e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007071596,"about_ca_system_score_gemma":0.00005710603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002177857,"about_ca_topic_score_gemma":0.000005856636,"domain_scores_codex":[0.9988428,0.00002784488,0.0006438741,0.00007034737,0.000319514,0.0000956263],"domain_scores_gemma":[0.998165,0.0002367048,0.0005451981,0.00004832293,0.0009574443,0.00004730085],"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":[0.0002164503,0.00005786791,0.005730131,0.00001480554,0.0000777927,0.00001045083,0.00212583,0.005148395,0.001409414,0.001544198,0.000004446264,0.9836602],"study_design_scores_gemma":[0.0004014488,0.000230085,0.06345642,0.0005407336,0.00002113346,0.000768083,0.001604086,0.9255302,0.00442648,0.002855219,0.00002464151,0.0001415074],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6383831,0.0002559874,0.3606148,0.0001878088,0.000407775,0.00005775218,0.000006929289,0.000005836666,0.00007998785],"genre_scores_gemma":[0.9920725,0.0002776854,0.007486933,0.00009281246,0.00006245122,2.79323e-7,0.00000225961,0.000002187882,0.000002931187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9835187,"threshold_uncertainty_score":0.3235284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02639264861899427,"score_gpt":0.3112548033929178,"score_spread":0.2848621547739235,"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."}}