{"id":"W2127927483","doi":"10.1109/titb.2006.864476","title":"High-Performance Medical Image Registration Using New Optimization Techniques","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Computer science; Image registration; Metric (unit); Implementation; Image (mathematics); Bounded function; Computation; Similarity (geometry); Parallel computing; Algorithm; Mathematical optimization; Artificial intelligence; 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":[0.0005420585,0.0002232746,0.0002544022,0.002385985,0.0001500081,0.00007118657,0.000694451,0.000611075,0.0001513051],"category_scores_gemma":[0.00004730778,0.0002128547,0.00003487647,0.002501335,0.0003078016,0.002778711,0.000008505021,0.0006503057,0.00003717725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002708669,"about_ca_system_score_gemma":0.0002463573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003733178,"about_ca_topic_score_gemma":0.00001317898,"domain_scores_codex":[0.9975791,0.0000393153,0.001028067,0.0002628299,0.0007772984,0.0003134125],"domain_scores_gemma":[0.9988336,0.00004936158,0.0003287931,0.0005134474,0.0001681583,0.000106629],"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":[0.00005007382,0.0003547148,0.00005969525,0.0001094883,0.00002336224,0.00002644255,0.0002116024,0.01867779,0.01781397,0.005681874,0.004453759,0.9525372],"study_design_scores_gemma":[0.001203194,0.0003474006,0.00006258635,0.0002519875,0.00001127437,0.0001211557,0.00004721453,0.3112297,0.6842616,0.001917823,0.000258187,0.0002878724],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002558034,0.00001109443,0.9877678,0.006969043,0.0003717628,0.0004543568,0.000004107386,0.001563943,0.0002998307],"genre_scores_gemma":[0.280768,0.0001231242,0.7179416,0.0009096193,0.0000642578,0.00007926265,0.00003344972,0.00001141915,0.00006925622],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9522493,"threshold_uncertainty_score":0.8679962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00797374032554557,"score_gpt":0.2587452523931287,"score_spread":0.2507715120675831,"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."}}