{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007389024,0.0002185699,0.0004473135,0.0002052235,0.0001655656,0.0004289121,0.0008746075,0.0001296516,0.001892314],"category_scores_gemma":[0.002135477,0.0001963586,0.0002077909,0.001743491,0.0004082066,0.0008134826,0.0004146308,0.0005006772,0.0001444276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002957141,"about_ca_system_score_gemma":0.00008116009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004306147,"about_ca_topic_score_gemma":0.00001211662,"domain_scores_codex":[0.9966576,0.0002891885,0.0005802182,0.0007316137,0.001343355,0.0003980194],"domain_scores_gemma":[0.9980039,0.0001935328,0.0002025999,0.0004165605,0.0001687832,0.001014601],"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":[0.00003276334,0.0002552109,0.003386695,0.0001733504,0.001251418,0.002150097,0.00391349,0.000003693211,0.04482337,0.0009446437,0.01974564,0.9233196],"study_design_scores_gemma":[0.001166369,0.0003497464,0.001612565,0.00003496154,0.0007871262,0.00004133614,0.0004843786,0.9688911,0.0182581,0.002347456,0.005328,0.0006988589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000643996,0.0002332392,0.9714569,0.02581445,0.00003746693,0.0001147218,0.000001231732,0.0004674757,0.001230499],"genre_scores_gemma":[0.1106,0.0007236307,0.8746031,0.01334074,0.000382514,0.00004375541,0.00007363385,0.00002797961,0.0002046735],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9688874,"threshold_uncertainty_score":0.9990201,"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."}}