{"id":"W2024697164","doi":"10.1118/1.4916657","title":"Neural‐network based autocontouring algorithm for intrafractional lung‐tumor tracking using Linac‐MR","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Imaging phantom; Centroid; Computer science; Artificial intelligence; Algorithm; Nuclear medicine; Lung tumor; Artificial neural network; Computer vision; Lung cancer; Medicine; Pathology","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":[0.0006812373,0.0004774742,0.0003128191,0.0004419111,0.0003297602,0.0003963909,0.000865719,0.0006755988,0.001481806],"category_scores_gemma":[0.001288441,0.0002374618,0.0003338792,0.0003715264,0.0002711466,0.0004871338,0.000317186,0.0005215098,0.0003682711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014644,"about_ca_system_score_gemma":0.0006524725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008340305,"about_ca_topic_score_gemma":0.009412006,"domain_scores_codex":[0.9997658,0.00003819801,0.00001234789,0.00006782568,0.0000970062,0.00001880476],"domain_scores_gemma":[0.9995649,0.0001430486,0.00006277598,0.00003742374,0.0001781275,0.00001358804],"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":[0.0002580762,0.00009987512,0.001720062,0.00007422287,0.00005947084,0.00009338676,0.0001202543,0.3810793,0.04278252,0.001703183,0.001130294,0.5708793],"study_design_scores_gemma":[0.000008305069,0.00004454343,0.0005130716,0.00000480853,0.00001112103,0.00003495488,0.000004407786,0.9883564,0.01014516,0.0002233172,0.0006467723,0.000007122675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03873324,0.0001615418,0.9586707,0.00006449119,0.00001914228,0.00005040548,0.00002122866,0.001065247,0.00121405],"genre_scores_gemma":[0.3507641,0.0001547611,0.6441249,0.0001123051,0.00001637598,0.0001598829,0.0001113895,0.0001540661,0.004402275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008340305,"threshold_uncertainty_score":0.0165835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02969351114587721,"score_gpt":0.3271771328530536,"score_spread":0.2974836217071763,"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."}}