{"id":"W4400121095","doi":"10.59275/j.melba.2024-151b","title":"Impact of Initialization on Intra-subject Pediatric Brain MR Image Registration: A Comparative Analysis between SyN ANTs and Deep Learning-Based Approaches","year":2024,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Initialization; Image registration; Artificial intelligence; Computer science; Subject (documents); Subject matter; Computer vision; Deep learning; Image (mathematics); Medical physics; Psychology; Medicine; Library science; Curriculum","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001607527,0.0001714707,0.0003716424,0.0005741231,0.000240204,0.0001402419,0.0004189766,0.00003947229,0.000006020286],"category_scores_gemma":[0.0004453001,0.000111996,0.0002126126,0.001871154,0.0002088411,0.0003572117,0.00005456295,0.0006517806,0.000001108469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006149748,"about_ca_system_score_gemma":0.0001010036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001385228,"about_ca_topic_score_gemma":0.000001644866,"domain_scores_codex":[0.9981479,0.0004542578,0.0005562108,0.0002229985,0.0004055627,0.0002130726],"domain_scores_gemma":[0.9965044,0.002484163,0.0005916346,0.0001687658,0.0001273246,0.0001237683],"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":[0.0004756895,0.0004074268,0.09934653,0.00039952,0.001975313,0.00005683779,0.006687365,0.6642432,0.00370655,0.003227077,0.0007803085,0.2186942],"study_design_scores_gemma":[0.0003997367,0.0005486896,0.01538271,0.00004819522,0.0003808247,0.00003866846,0.00004556819,0.9819998,0.0001321415,0.0006648742,0.0002407993,0.0001180671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07390275,0.0008678368,0.9217128,0.003217721,0.00003606429,0.0001586976,0.00000610402,0.00005211174,0.00004595417],"genre_scores_gemma":[0.9895644,0.00005097309,0.01004804,0.00004215186,0.0002316664,0.000004494939,0.00003677372,0.00001217942,0.000009311977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9156616,"threshold_uncertainty_score":0.4567064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03619910164753889,"score_gpt":0.3430835035040612,"score_spread":0.3068844018565223,"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."}}