{"id":"W2049104356","doi":"10.1118/1.4814282","title":"SU‐E‐J‐70: Intra and Intermodality Validation of Registration Algorithms On a Deformable Phantom","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôtel-Dieu de Québec","funders":"","keywords":"Imaging phantom; Image registration; Metric (unit); Computer science; Deformation (meteorology); Artificial intelligence; Computer vision; Medical imaging; Translation (biology); Algorithm; Mutual information; Nuclear medicine; Mathematics; Image (mathematics); Medicine; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002101198,0.00008208468,0.0001574849,0.00002295885,0.0000234345,0.00002425187,0.0000751624,0.00005568813,0.0001309455],"category_scores_gemma":[0.0001079627,0.00006640283,0.00003519718,0.000114799,0.00008952742,0.0001396921,0.00001647473,0.0001634624,0.00002437622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001800043,"about_ca_system_score_gemma":0.00001404425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001258769,"about_ca_topic_score_gemma":5.623272e-7,"domain_scores_codex":[0.9991676,0.00001916932,0.0002093846,0.0001002458,0.0003661135,0.0001375089],"domain_scores_gemma":[0.9996123,0.00005258829,0.00003456302,0.0001257897,0.00003701365,0.0001377822],"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":[0.000003683016,0.0001748532,0.001525826,0.0003676651,0.0001291015,0.000004625592,0.0005205421,0.0008848637,0.003837799,0.0006681272,0.008201375,0.9836816],"study_design_scores_gemma":[0.0004657306,0.00004971568,0.0007815376,0.0001861761,0.0000439968,0.000002993457,0.0000430569,0.8882727,0.1009161,0.008722136,0.0003417093,0.0001741592],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.650243,0.00005199341,0.3447679,0.0007560242,0.000158329,0.00009748813,0.000004381704,0.0001202204,0.003800687],"genre_scores_gemma":[0.9993713,0.00004481373,0.0002192775,0.0001232157,0.0001600574,0.00000887514,0.00002801704,0.000008274228,0.00003615335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9835074,"threshold_uncertainty_score":0.2707829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212353196291362,"score_gpt":0.2420372499539657,"score_spread":0.2299137179910521,"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."}}