{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002465144,0.002642473,0.001505845,0.002353788,0.0007355143,0.002907235,0.00414315,0.001271716,0.07036633],"category_scores_gemma":[0.005087564,0.001415797,0.001693646,0.00131446,0.0005165928,0.001587857,0.002141534,0.001686445,0.03014988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001123186,"about_ca_system_score_gemma":0.002151647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004406254,"about_ca_topic_score_gemma":0.004461996,"domain_scores_codex":[0.9988502,0.0001775723,0.0001047691,0.0003258156,0.0003932677,0.0001482534],"domain_scores_gemma":[0.9982281,0.000611245,0.00009689432,0.0003051567,0.0006548308,0.0001037741],"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":[0.003267723,0.0002528562,0.003526403,0.002285843,0.0005720936,0.001056826,0.0004240506,0.02204963,0.04190117,0.01074652,0.4686797,0.4452371],"study_design_scores_gemma":[0.0008302673,0.0003523346,0.006669665,0.0002888108,0.000221903,0.002468295,0.0001777936,0.4493947,0.1079465,0.02792332,0.4032393,0.0004871956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004825693,0.0005277806,0.5815948,0.0002499382,0.0001604377,0.00080397,0.01387928,0.3906864,0.007271702],"genre_scores_gemma":[0.06706426,0.0008439451,0.8049953,0.0008684691,0.0001439504,0.00366483,0.05261939,0.05625672,0.01354318],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07036633,"threshold_uncertainty_score":0.2353988,"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."}}