{"id":"W2018757344","doi":"10.1109/isbi.2012.6235651","title":"Locally-adaptive similarity metric for deformable medical image registration","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Cancer Institute","keywords":"Metric (unit); Weighting; Artificial intelligence; Image registration; Pattern recognition (psychology); Ranking (information retrieval); Similarity (geometry); Computer science; Set (abstract data type); Image (mathematics); Computer vision; Medical imaging","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":[0.001567332,0.0001043701,0.0001302037,0.0001184231,0.00009750567,0.00008571844,0.0005742372,0.0001059597,0.0002651316],"category_scores_gemma":[0.0008333364,0.00008343386,0.00006006267,0.0004296767,0.00008155694,0.001850628,0.0001448248,0.0001341854,0.00005117747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007563138,"about_ca_system_score_gemma":0.0001212123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004776592,"about_ca_topic_score_gemma":0.000008489335,"domain_scores_codex":[0.9983941,0.00006045368,0.0002939487,0.000208263,0.0006844715,0.0003587219],"domain_scores_gemma":[0.9988496,0.0002330652,0.00009273524,0.0003161102,0.0001632665,0.0003452263],"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":[0.00003148354,0.0006030807,0.0003629724,0.00008320469,0.00004827945,0.000009576683,0.0005675891,0.000002445711,0.002097113,0.2711459,0.1751395,0.5499089],"study_design_scores_gemma":[0.001802679,0.0006402067,0.001358843,0.00004956638,0.00003277769,0.00007826193,0.000192457,0.4670807,0.4970511,0.01949784,0.01146113,0.0007544475],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005647114,0.00005543652,0.9857591,0.001245935,0.0001854016,0.0003485045,0.000001633235,0.0003876541,0.0119599],"genre_scores_gemma":[0.08026327,0.00001599411,0.9163272,0.002544113,0.0001229172,0.00008499978,0.000008478461,0.00000692329,0.0006261138],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5491544,"threshold_uncertainty_score":0.3402334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03119996756101343,"score_gpt":0.3203490906908453,"score_spread":0.2891491231298319,"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."}}