{"id":"W3037946733","doi":"10.1088/1361-6560/ab9fcb","title":"Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy","year":2020,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Radiation Therapy and Dosimetry","field":"Medicine","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Imaging phantom; Proton therapy; Nuclear medicine; Head and neck; Cone beam computed tomography; Projection (relational algebra); Context (archaeology); Monte Carlo method; Convolutional neural network; Computer science; Artificial intelligence; Mathematics; Medicine; Radiation therapy; Computed tomography; Radiology; Algorithm; Statistics; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000956566,0.0007985895,0.0003626625,0.0004435877,0.0001860472,0.000452667,0.0008510796,0.0006839762,0.0008814815],"category_scores_gemma":[0.003023264,0.0002721541,0.0003836117,0.0003177468,0.0002774367,0.0003137217,0.0004718803,0.0004933162,0.0001863041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271195,"about_ca_system_score_gemma":0.000991105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02073247,"about_ca_topic_score_gemma":0.01676378,"domain_scores_codex":[0.999715,0.00006437044,0.00001376592,0.0000560384,0.000110629,0.00004008667],"domain_scores_gemma":[0.9991828,0.0004067151,0.00006875702,0.00005223704,0.0002468792,0.00004264102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001044282,0.0002423669,0.009231791,0.0001649141,0.0002032592,0.0001530103,0.0000593808,0.7863853,0.02236561,0.0004805034,0.001234542,0.178435],"study_design_scores_gemma":[0.00001000615,0.00008659465,0.001294747,0.000005579277,0.00001925083,0.00002479171,0.000006465782,0.9919241,0.006365081,0.00008351681,0.0001751387,0.000004693036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8240175,0.001697909,0.1674048,0.0004732008,0.0001235754,0.000149411,0.0004769885,0.00277789,0.002878651],"genre_scores_gemma":[0.9643998,0.0002539464,0.03342304,0.00007805062,0.00001211462,0.00004220634,0.0004645812,0.0001124034,0.001213778],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02073247,"threshold_uncertainty_score":0.04122359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3497411306886827,"score_gpt":0.4363797522282629,"score_spread":0.08663862153958024,"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."}}