{"id":"W4387429075","doi":"10.31219/osf.io/k23r9","title":"Hessian-based Similarity Metric for Multimodal Medical Image Registration","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Image registration; Metric (unit); Hessian matrix; Artificial intelligence; Affine transformation; Robustness (evolution); Similarity (geometry); Computer science; Medical imaging; Pattern recognition (psychology); Computation; Context (archaeology); Computer vision; Mathematics; Image (mathematics); Algorithm; Geometry; Applied 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001622606,0.0004684294,0.0007372395,0.001071649,0.0002647808,0.0009502152,0.0008537255,0.0008730727,0.001680014],"category_scores_gemma":[0.006669193,0.0002685113,0.0004961875,0.001060045,0.001001955,0.001463789,0.001180455,0.0008720551,0.0005898592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008956447,"about_ca_system_score_gemma":0.0008028808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001163048,"about_ca_topic_score_gemma":0.001231758,"domain_scores_codex":[0.9987925,0.0003838121,0.00009095843,0.0001669647,0.000524628,0.00004113132],"domain_scores_gemma":[0.9986958,0.0004991262,0.0002168951,0.0002172491,0.0002955813,0.00007530846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003062332,0.0001318094,0.001976653,0.0003740846,0.0001617805,0.000304609,0.0002159911,0.3897557,0.09333712,0.1296725,0.005426187,0.3783374],"study_design_scores_gemma":[0.00001441797,0.0001160041,0.00130487,0.00001199737,0.00001789393,0.0003268538,0.00002704339,0.9543838,0.01067424,0.03083045,0.002260094,0.00003236809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009851759,0.0002435257,0.9886651,0.0001601675,0.00001913822,0.00003165337,0.00004474642,0.0002411664,0.0007427022],"genre_scores_gemma":[0.4335276,0.0005382505,0.5626199,0.000132606,0.00008494941,0.0001267301,0.0002612537,0.0002123641,0.002496326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001680014,"threshold_uncertainty_score":0.008581221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06153101939060815,"score_gpt":0.3776232136431117,"score_spread":0.3160921942525036,"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."}}