{"id":"W1133070690","doi":"10.1118/1.4924145","title":"SU-E-J-58: Comparison of Conformal Tracking Methods Using Initial, Adaptive and Preceding Image Frames for Image Registration","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba","funders":"","keywords":"Computer vision; Artificial intelligence; Optical flow; Image registration; Computer science; Standard deviation; Lung tumor; Tracking (education); Nuclear medicine; Aperture (computer memory); Image-guided radiation therapy; Lung cancer; Medical imaging; Image (mathematics); Mathematics; Medicine; Physics","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":[0.001870597,0.0005255645,0.0004489669,0.001031262,0.0002255707,0.0007234742,0.0006799725,0.0005246273,0.001614001],"category_scores_gemma":[0.006501337,0.0002544272,0.0004116332,0.0006803397,0.000191996,0.0005677178,0.0003597155,0.0003719807,0.0004798124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003129003,"about_ca_system_score_gemma":0.0005353036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002883128,"about_ca_topic_score_gemma":0.003178136,"domain_scores_codex":[0.9991673,0.0002194813,0.00008323511,0.0001266804,0.0003428088,0.00006048009],"domain_scores_gemma":[0.9979625,0.0009008073,0.0002040179,0.00026787,0.0005942537,0.00007060009],"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":[0.003201712,0.000322297,0.02047773,0.0004729553,0.0003642134,0.00008134308,0.0002575373,0.03850558,0.05588983,0.001270831,0.003258356,0.8758975],"study_design_scores_gemma":[0.0004843454,0.003595843,0.0724824,0.00007608905,0.0003616678,0.001651907,0.0002142878,0.7801152,0.1311705,0.0008576549,0.008801989,0.0001881002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5068564,0.002266652,0.4791122,0.000129303,0.0002678932,0.0002452427,0.0003847687,0.005297901,0.005439686],"genre_scores_gemma":[0.7480172,0.0005185834,0.2473675,0.00006707279,0.00003652086,0.0001020594,0.001065432,0.0006479181,0.002177702],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002883128,"threshold_uncertainty_score":0.009892821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1062160502783826,"score_gpt":0.4602162821614691,"score_spread":0.3540002318830865,"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."}}