{"id":"W1617148189","doi":"10.1109/iembs.2000.901440","title":"Automated prostate implant seed localization from fluoroscopic images using simulated annealing","year":2002,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôtel-Dieu de Québec; Centre hospitalier universitaire de Québec; Université Laval","funders":"","keywords":"Imaging phantom; Simulated annealing; Fluoroscopy; Computer science; Artificial intelligence; Computer vision; Image matching; Radiography; Prostate; Biomedical engineering; Nuclear medicine; Radiology; Algorithm; Medicine; Image (mathematics)","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":[0.00004495427,0.000158441,0.0001908116,0.0001060799,0.00009361489,0.00008361321,0.00006841707,0.0000757047,0.0001813394],"category_scores_gemma":[0.00001030746,0.0001479635,0.00004682223,0.0002940643,0.0000113285,0.0001349095,0.00001418913,0.00007653556,0.00006194771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004857784,"about_ca_system_score_gemma":0.000003370078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004161798,"about_ca_topic_score_gemma":0.000007988765,"domain_scores_codex":[0.9991751,0.00001896374,0.0002656488,0.0001802164,0.0001200727,0.0002399607],"domain_scores_gemma":[0.9996821,0.0000222843,0.0000268984,0.0001549957,0.00004931082,0.00006445464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000106246,0.000007675801,0.0009685048,0.00001653922,0.00004805348,0.000008904499,0.0001967898,0.977029,0.02103811,5.775315e-7,0.000365725,0.0003190108],"study_design_scores_gemma":[0.0002455083,0.000006126183,0.00009554435,0.00003938365,0.00005555268,0.000002029711,0.00003131513,0.9838619,0.01542995,0.00002289272,0.00002474057,0.0001850918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7394576,0.0004092977,0.2569895,0.00001482689,0.0001116885,0.00007472531,0.0000326455,0.002680215,0.0002295438],"genre_scores_gemma":[0.9965685,0.00004772217,0.003036274,0.00004391944,0.00004122427,6.634145e-7,0.0001034317,0.00003975398,0.0001185141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2571109,"threshold_uncertainty_score":0.6033778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525367478059243,"score_gpt":0.2266966764812897,"score_spread":0.2114430017006972,"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."}}