{"id":"W2396856533","doi":"10.82308/1390","title":"Efficient and reliable methods for direct parameterized image registration","year":2008,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parameterized complexity; Computer science; Hessian matrix; Image registration; Measure (data warehouse); Context (archaeology); Mathematical optimization; Algorithm; Range (aeronautics); Pixel; Reliability (semiconductor); Image (mathematics); Mathematics; Artificial intelligence; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003157975,0.001178328,0.001430117,0.001340097,0.000774192,0.002000571,0.002308813,0.001657775,0.004696265],"category_scores_gemma":[0.01444332,0.001077906,0.001176073,0.001226481,0.002181351,0.002578112,0.004046419,0.002574123,0.003129872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007412307,"about_ca_system_score_gemma":0.001226907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000784985,"about_ca_topic_score_gemma":0.001023209,"domain_scores_codex":[0.9957617,0.001113813,0.000188217,0.0006113298,0.002177968,0.00014698],"domain_scores_gemma":[0.9950532,0.002162384,0.0004891866,0.001494854,0.0007158454,0.00008454882],"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":[0.0001118146,0.0000737785,0.0004765352,0.0003289294,0.0000852422,0.0001728625,0.000381515,0.3481467,0.03193215,0.1933615,0.004003166,0.4209257],"study_design_scores_gemma":[0.00002490388,0.00006230986,0.0002047814,0.00004678102,0.00001809468,0.0002124708,0.00004661686,0.9068472,0.01005759,0.06783045,0.01460442,0.00004439563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005427789,0.00009501093,0.9986076,0.00003815052,0.000009711552,0.00002161043,0.000006708,0.0001449612,0.0005335074],"genre_scores_gemma":[0.03591099,0.0004379176,0.9594063,0.00005978784,0.00006006295,0.0002283552,0.0000730216,0.0004429506,0.003380772],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004696265,"threshold_uncertainty_score":0.01670116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03360948732611684,"score_gpt":0.3152064275849495,"score_spread":0.2815969402588327,"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."}}