{"id":"W2958287596","doi":"10.1016/j.ctro.2019.07.003","title":"Learning from scanners: Bias reduction and feature correction in radiomics","year":2019,"lang":"en","type":"article","venue":"Clinical and Translational Radiation Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Scanner; Imaging phantom; Standard deviation; Feature (linguistics); Artificial intelligence; Region of interest; Pattern recognition (psychology); Signal-to-noise ratio (imaging); Computer science; Nuclear medicine; Medicine; Mathematics; Statistics","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":[0.0006105766,0.00008847496,0.0003041377,0.0001161613,0.00005267629,0.00001554098,0.0000222947,0.0002460967,0.00007439265],"category_scores_gemma":[0.0003803938,0.00008317512,0.00005168073,0.0001222971,0.000136501,0.00010098,0.000007256297,0.000765017,0.00001068565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002913847,"about_ca_system_score_gemma":0.0001170009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006465634,"about_ca_topic_score_gemma":0.00001095762,"domain_scores_codex":[0.9989245,0.0001934831,0.0003476181,0.0003003717,0.0001172651,0.0001167673],"domain_scores_gemma":[0.9989393,0.0007385737,0.000106135,0.00005781424,0.00003418409,0.0001239803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002801557,0.00006637349,0.6882752,0.000009729316,0.00002544559,0.000004396558,0.0002773897,0.0007227837,0.0001886725,0.0001892854,0.0002623793,0.3096983],"study_design_scores_gemma":[0.003920374,0.0003818753,0.8202925,0.00005207554,0.00004001342,0.00008098232,0.0001051546,0.1410742,0.000007485092,0.0007429252,0.03321984,0.00008264891],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982161,0.0005933073,0.000476245,0.0143607,0.0009468005,0.0002939594,0.000001825694,0.00002856113,0.001137643],"genre_scores_gemma":[0.9956338,0.001064551,0.001566741,0.0005348361,0.0004981122,0.000008081509,0.0001602671,0.00001122382,0.00052232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3096156,"threshold_uncertainty_score":0.3391783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03024073712173539,"score_gpt":0.3625006089002208,"score_spread":0.3322598717784855,"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."}}