{"id":"W3011129081","doi":"10.1117/1.jmi.7.4.042803","title":"Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines","year":2020,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Upload; Computer science; Software; Pipeline (software); Graphical user interface; Segmentation; Feature (linguistics); Interface (matter); Image processing; Application programming interface; Image file formats; Source code; Machine learning; Artificial intelligence; Image (mathematics); World Wide Web","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":[0.004658775,0.003555753,0.001665348,0.004680369,0.0007168285,0.003238525,0.004463027,0.00200252,0.08191197],"category_scores_gemma":[0.01369169,0.001965102,0.002192387,0.002309803,0.0007395441,0.003950284,0.004787895,0.002699996,0.04283467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001273835,"about_ca_system_score_gemma":0.002345297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003150129,"about_ca_topic_score_gemma":0.002867325,"domain_scores_codex":[0.9986063,0.0001967799,0.0001814547,0.0003127886,0.0005510093,0.0001516893],"domain_scores_gemma":[0.9944525,0.002704655,0.0004233278,0.0007767255,0.001185759,0.0004569719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001908847,0.0003329614,0.003704683,0.002411559,0.0003935426,0.0008841977,0.0004455573,0.006999557,0.01681148,0.009088988,0.7350772,0.2219414],"study_design_scores_gemma":[0.002003327,0.0005087461,0.01157793,0.0009898153,0.0003345117,0.002035412,0.0002504058,0.1733086,0.07042198,0.05768201,0.6799436,0.0009437748],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.001576909,0.0003906025,0.3096719,0.0004321334,0.0001662755,0.0005843908,0.02788951,0.6545164,0.004771945],"genre_scores_gemma":[0.0584799,0.001500399,0.5965936,0.002508368,0.0003759268,0.005323275,0.1315845,0.1877932,0.01584084],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.08191197,"threshold_uncertainty_score":0.2740228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133768374042713,"score_gpt":0.3554667564339915,"score_spread":0.3341290726935644,"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."}}