{"id":"W2094809467","doi":"10.1109/leos.2007.4382282","title":"fMRI and DOI Non-Rigid Registration with Discrete Curvature Flows and Mutual Information","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Université de Sherbrooke","funders":"","keywords":"Wavelength; Image resolution; Mutual information; Optics; SIGNAL (programming language); Resolution (logic); Physics; Absorption (acoustics); Curvature; Nuclear magnetic resonance; Materials science; Artificial intelligence; Computer science; Mathematics; Geometry","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":[0.002234283,0.0006908519,0.0008155797,0.001637936,0.0003327057,0.001166458,0.0007005796,0.001083058,0.00197812],"category_scores_gemma":[0.005605976,0.0006679428,0.0008335877,0.001451441,0.0009799411,0.001709591,0.001769158,0.001054666,0.0008206289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006602093,"about_ca_system_score_gemma":0.001004576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001647404,"about_ca_topic_score_gemma":0.002427779,"domain_scores_codex":[0.9991294,0.0003782603,0.00005145156,0.0001741045,0.0002135653,0.00005325269],"domain_scores_gemma":[0.9991232,0.0003310548,0.0001978397,0.000168587,0.0001312734,0.00004806415],"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":[0.001556297,0.0002358681,0.00308423,0.0005887159,0.0003571757,0.000323957,0.0004451517,0.2854757,0.07550317,0.07960667,0.003838387,0.5489847],"study_design_scores_gemma":[0.00002929725,0.0001381729,0.002201924,0.00002361219,0.00003043978,0.0003908216,0.00003336102,0.9542087,0.01989382,0.01884257,0.004152873,0.00005444575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01915735,0.0003814909,0.9780964,0.0002259601,0.000044919,0.00006618004,0.0001440144,0.0006124626,0.001271195],"genre_scores_gemma":[0.341335,0.000550774,0.6507046,0.000123893,0.000114151,0.0003662767,0.000584109,0.0003435668,0.00587768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002234283,"threshold_uncertainty_score":0.01181614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009780550222392362,"score_gpt":0.2740327088662691,"score_spread":0.2642521586438767,"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."}}