{"id":"W3111025719","doi":"10.1016/j.media.2020.101939","title":"Image registration: Maximum likelihood, minimum entropy and deep learning","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Canadian Institutes of Health Research; National Cancer Institute; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Ontario Trillium Foundation","keywords":"Mutual information; Pairwise comparison; Artificial intelligence; Image registration; Computer science; Discriminative model; Entropy (arrow of time); Kullback–Leibler divergence; Metric (unit); Pattern recognition (psychology); Maximum likelihood; Iterative method; Principle of maximum entropy; Upper and lower bounds; Mathematics; Algorithm; Image (mathematics); Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002462351,0.0008422743,0.0014924,0.001372782,0.0004012754,0.001500853,0.001520898,0.002038751,0.002103908],"category_scores_gemma":[0.006543073,0.0008894094,0.0009587301,0.001529161,0.00116166,0.002401983,0.001995366,0.002296162,0.0008629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083991,"about_ca_system_score_gemma":0.001294512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004110376,"about_ca_topic_score_gemma":0.005606946,"domain_scores_codex":[0.9992581,0.0002651931,0.00004564055,0.0001660552,0.0002153861,0.00004966226],"domain_scores_gemma":[0.9982631,0.0009113086,0.0002622481,0.0002501742,0.0002340462,0.00007914883],"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":[0.0003691578,0.0001519298,0.001344081,0.0002732689,0.0001658928,0.00008352027,0.00009333937,0.3732265,0.01069536,0.04509308,0.004843588,0.5636603],"study_design_scores_gemma":[0.000008019934,0.00002921548,0.0002952418,0.00001437251,0.00001556671,0.00005248395,0.000005546681,0.9772575,0.002925486,0.01868258,0.0007000628,0.00001403334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005263111,0.0007569265,0.9922143,0.0003670889,0.00003177913,0.00002243934,0.00006114894,0.0007774957,0.0005057722],"genre_scores_gemma":[0.3116325,0.001550139,0.6771259,0.0002832652,0.0002387246,0.0001424977,0.0004102929,0.0007591918,0.00785752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004110376,"threshold_uncertainty_score":0.01302236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009946999533290153,"score_gpt":0.2688214014429026,"score_spread":0.2588744019096124,"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."}}