{"id":"W4317496750","doi":"10.1109/access.2023.3238058","title":"GMCNet: A Generative Multi-Resolution Framework for Cardiac Registration","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Image registration; Robustness (evolution); Mutual information; Pairwise comparison; Convolutional neural network; Pattern recognition (psychology); Generative model; Computer vision; Deep learning; Metric (unit); Artificial neural network; Generative grammar; Image (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":[0.001262424,0.0009975287,0.0008831978,0.001073012,0.0003884375,0.0008956544,0.002477036,0.001509067,0.002415334],"category_scores_gemma":[0.002878222,0.0007718888,0.001433518,0.001032348,0.0006738642,0.001142548,0.001821068,0.002251524,0.001009603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009656828,"about_ca_system_score_gemma":0.00123078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007328333,"about_ca_topic_score_gemma":0.01185411,"domain_scores_codex":[0.9995281,0.0001153323,0.00002343033,0.0001162664,0.0001720733,0.00004485936],"domain_scores_gemma":[0.9995078,0.000179556,0.0000726959,0.0001018589,0.0001043939,0.00003375759],"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":[0.0001019144,0.00005342127,0.0006721591,0.00008795038,0.0001298433,0.0001806338,0.0000565821,0.8010747,0.007269646,0.01310803,0.002992902,0.1742722],"study_design_scores_gemma":[0.000003905646,0.00001135714,0.00007899542,0.000005909911,0.000006640352,0.00005387456,0.000002922743,0.9943177,0.001788232,0.002719718,0.001002353,0.000008409415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002075881,0.0001523246,0.996126,0.00005899982,0.00002110105,0.0000274753,0.0000722754,0.0009865408,0.0004793101],"genre_scores_gemma":[0.2060747,0.0006295964,0.7855743,0.0003581043,0.00006357727,0.0002985957,0.001134168,0.0009226778,0.004944363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007328333,"threshold_uncertainty_score":0.01457137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1098266306934959,"score_gpt":0.4168391987583695,"score_spread":0.3070125680648736,"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."}}