{"id":"W2020073831","doi":"10.1118/1.4734988","title":"SU-E-J-151: Evaluation of a Real Time Tumour Autocontouring Algorithm Using In-Vivo Lung MR Images with Various Contrast to Noise Ratios","year":2012,"lang":"en","type":"article","venue":"Medical Physics","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Centroid; Contouring; Nuclear medicine; Sagittal plane; Displacement (psychology); Voxel; Contrast (vision); Mathematics; Noise (video); Medicine; Algorithm; Artificial intelligence; Computer science; Image (mathematics); Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001505364,0.0005112984,0.0002988182,0.0005295363,0.0001497426,0.0006479344,0.0005999357,0.000829879,0.0009448423],"category_scores_gemma":[0.003523007,0.000176851,0.0002575215,0.0003126379,0.000216438,0.000424689,0.0001899344,0.0002513636,0.0002463068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004496962,"about_ca_system_score_gemma":0.0003342706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001334461,"about_ca_topic_score_gemma":0.001492332,"domain_scores_codex":[0.9996253,0.0001000569,0.00002756564,0.00006393489,0.0001572492,0.00002585338],"domain_scores_gemma":[0.9988757,0.0004566655,0.000136159,0.0001198977,0.0003493068,0.00006240647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00357137,0.0005188779,0.01537615,0.000510848,0.0002854655,0.0004049134,0.0001795247,0.09824912,0.5555009,0.000870276,0.001207591,0.3233249],"study_design_scores_gemma":[0.0001644365,0.002759538,0.02821187,0.00002330829,0.0001615751,0.001644143,0.00005606088,0.6066161,0.358098,0.0001923042,0.001997731,0.00007498427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8603033,0.001167782,0.1335967,0.0001283524,0.00005257388,0.0001279276,0.0001407601,0.002483053,0.001999657],"genre_scores_gemma":[0.834226,0.0002385937,0.1635409,0.00007491685,0.00001076872,0.00005940047,0.0003509423,0.0002515696,0.001246972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001505364,"threshold_uncertainty_score":0.007961214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02400106897731594,"score_gpt":0.3263803824145345,"score_spread":0.3023793134372186,"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."}}