{"id":"W2893031952","doi":"10.3389/fninf.2018.00064","title":"APPIAN: Automated Pipeline for PET Image Analysis","year":2018,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; Jewish General Hospital; McGill University","funders":"Canadian Institutes of Health Research; National Institutes of Health","keywords":"Computer science; Pipeline (software); Modular design; Visualization; Image processing; Automated method; Artificial intelligence; Region of interest; Pipeline transport; Computer vision; Data mining; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002284606,0.0001192462,0.0003396102,0.0003733221,0.00006358862,0.00002643106,0.0001487147,0.00004274466,0.00003292607],"category_scores_gemma":[0.0002648305,0.0001037848,0.0001253413,0.0008771143,0.000170484,0.0001107235,0.00003863427,0.0001434439,0.00002012007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003862042,"about_ca_system_score_gemma":0.0000482801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004927361,"about_ca_topic_score_gemma":0.000001698235,"domain_scores_codex":[0.9989446,0.000009892318,0.0004760072,0.0001340358,0.0001814452,0.0002539495],"domain_scores_gemma":[0.9991612,0.00003401419,0.0001119567,0.0004222246,0.0001349262,0.0001356769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004978632,0.0001074477,0.003010313,0.0001298016,0.00008425272,0.00001021906,0.000218009,0.000009386293,0.0002868693,0.0002022855,0.9921601,0.00373154],"study_design_scores_gemma":[0.0006144845,0.00008178763,0.0008685986,0.00002593345,0.0002927399,0.00001627845,0.00009243358,0.9318907,0.0005781796,0.0002404456,0.06520289,0.00009557919],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01667269,0.0000111561,0.9727519,0.003641016,0.0002064417,0.0008385142,0.00004349632,0.000707386,0.005127438],"genre_scores_gemma":[0.06961782,0.00004789089,0.926986,0.002223353,0.0001267506,0.00007914572,0.0001986338,0.00001999514,0.000700378],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9318812,"threshold_uncertainty_score":0.4232223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339028247207533,"score_gpt":0.3137097533218315,"score_spread":0.3003194708497561,"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."}}