{"id":"W1991691155","doi":"10.1117/12.467198","title":"Nonrigid mammogram registration using mutual information","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Mutual information; Computer science; Artificial intelligence; Centroid; Image registration; Computer vision; Point set registration; Matching (statistics); Pattern recognition (psychology); Similarity (geometry); Landmark; Transformation (genetics); Spline (mechanical); Similarity measure; Point (geometry); Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003106238,0.0002872416,0.0002987605,0.0001387011,0.00008463686,0.0001829462,0.0004636916,0.0002165338,0.00002171243],"category_scores_gemma":[0.0002367301,0.0002673171,0.0003785327,0.0003763516,0.0001176431,0.001127233,0.00004677039,0.0002457024,0.000004836266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001944722,"about_ca_system_score_gemma":0.00001067406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007497083,"about_ca_topic_score_gemma":1.515323e-7,"domain_scores_codex":[0.9980745,1.077498e-8,0.0007681167,0.0001980198,0.0006165173,0.0003428449],"domain_scores_gemma":[0.9984474,0.00005764053,0.0002660068,0.00005932266,0.001065754,0.000103861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005075255,0.0001252255,0.0003330843,0.001098251,0.0004844693,1.439601e-7,0.0005964363,0.08531267,0.4269429,0.472758,0.01070867,0.001589492],"study_design_scores_gemma":[0.0006147559,0.0001232838,0.0001247618,0.0001528643,0.00009323507,0.00001453143,0.0004408907,0.9544598,0.03756834,0.0002990655,0.005801111,0.0003073882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991967,0.000072202,0.001859662,0.0004523289,0.0003379892,0.0004317987,0.00003383621,0.0001844088,0.004660824],"genre_scores_gemma":[0.8740473,0.000191251,0.124885,0.00009328254,0.0004783166,0.00006312667,0.00003747787,0.00008169153,0.0001225056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8691471,"threshold_uncertainty_score":0.9999779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01463722919284243,"score_gpt":0.2117921902531272,"score_spread":0.1971549610602848,"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."}}