{"id":"W2134119739","doi":"10.1118/1.1414308","title":"Automated seed detection and three‐dimensional reconstruction. I. Seed localization from fluoroscopic images or radiographs","year":2001,"lang":"en","type":"article","venue":"Medical Physics","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier de l'Université Laval; Centre hospitalier universitaire de Québec","funders":"","keywords":"Thresholding; Artificial intelligence; Orientation (vector space); Computer vision; Computer science; Radiography; Normalization (sociology); Image processing; Image intensifier; Nuclear medicine; Pattern recognition (psychology); Mathematics; Medicine; Radiology; Image (mathematics); Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007467374,0.0006109105,0.0006324491,0.001967474,0.0003088715,0.0007615159,0.0008801093,0.0009229906,0.002848846],"category_scores_gemma":[0.0031834,0.0006626365,0.0005169671,0.0008786998,0.0004657542,0.0006035903,0.0005805168,0.0004094104,0.002169966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003047829,"about_ca_system_score_gemma":0.0004553215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001665383,"about_ca_topic_score_gemma":0.002070622,"domain_scores_codex":[0.9991605,0.0001779298,0.00004148375,0.0001461835,0.0004093359,0.00006453888],"domain_scores_gemma":[0.9986279,0.0006373004,0.0001659366,0.0002280482,0.0003067038,0.00003418806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004227982,0.00008229926,0.002200077,0.0002744994,0.00008418081,0.0003034114,0.0001411215,0.00926888,0.3065392,0.001322446,0.002831618,0.6765294],"study_design_scores_gemma":[0.0001701762,0.0005592317,0.03055651,0.00008274953,0.0001326346,0.00618746,0.0001034672,0.4794377,0.4565538,0.003286946,0.02272244,0.0002069345],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02897051,0.0003991557,0.9654647,0.00005712436,0.0000265837,0.0001123129,0.0001473773,0.003995569,0.0008265309],"genre_scores_gemma":[0.1321015,0.0003033341,0.8649682,0.0000627295,0.00002707086,0.0001961588,0.0004131058,0.0002767739,0.001651183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002848846,"threshold_uncertainty_score":0.009530306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007884930545832104,"score_gpt":0.2348292264685266,"score_spread":0.2269442959226944,"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."}}