{"id":"W2965102400","doi":"10.1007/s10278-019-00255-7","title":"Imager-4D: New Software for Viewing Dynamic PET Scans and Extracting Radiomic Parameters from PET Data","year":2019,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; Foundation for the National Institutes of Health","keywords":"Computer science; Software; Computer vision; Artificial intelligence; Medical physics; Positron emission tomography; Computer graphics (images); Nuclear medicine; Medicine","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":[0.0003185093,0.0001586213,0.0003999037,0.0001243959,0.00006737498,0.0003671125,0.000305134,0.0000175095,0.00002716968],"category_scores_gemma":[0.0008178964,0.0001334648,0.00010624,0.000112336,0.00007856569,0.001284821,0.0001431711,0.000341165,0.000005345098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007122946,"about_ca_system_score_gemma":0.0002229297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002738593,"about_ca_topic_score_gemma":4.345758e-7,"domain_scores_codex":[0.9986727,0.000009957163,0.0005328786,0.0002810157,0.0002555893,0.0002478474],"domain_scores_gemma":[0.9983442,0.0004040466,0.0003930497,0.0004481772,0.0001201564,0.0002903402],"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":[0.0003576766,0.0004173568,0.1189464,0.0005424907,0.0005490699,0.000808216,0.0007139515,0.00004014595,0.07559974,0.00005620473,0.0467327,0.7552361],"study_design_scores_gemma":[0.01999421,0.0009617535,0.0177901,0.01101216,0.00311231,0.04122736,0.003481233,0.7349753,0.005326289,0.0231411,0.136564,0.002414157],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6547819,0.0008843328,0.3358281,0.007593258,0.0001772537,0.0003885532,0.0001506397,0.00007077146,0.0001252363],"genre_scores_gemma":[0.6600903,0.00006940097,0.3389512,0.0004337751,0.0001446544,0.000002179378,0.0001256422,0.00003415202,0.0001487761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7528219,"threshold_uncertainty_score":0.5442537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03677907973758864,"score_gpt":0.3358955319840746,"score_spread":0.2991164522464859,"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."}}