{"id":"W3025036301","doi":"10.1002/mp.13678","title":"Machine and deep learning methods for radiomics","year":2020,"lang":"en","type":"review","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":583,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Cancer Institute","keywords":"Radiomics; Artificial intelligence; Computer science; Deep learning; Machine learning; Medical imaging; Standardization; Data science; Translational research; Precision medicine; Big data; Medical physics; Medicine; Data mining; Pathology","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001314125,0.0005303234,0.003037513,0.00008467665,0.0001601318,0.00004841785,0.0002807901,0.0004917299,0.00007926852],"category_scores_gemma":[0.005644344,0.0004092286,0.0006816892,0.0003245593,0.0003927562,0.00004058529,0.0002117535,0.002923203,0.00002005592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001024535,"about_ca_system_score_gemma":0.000457246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006072138,"about_ca_topic_score_gemma":1.290201e-7,"domain_scores_codex":[0.9972717,0.0003216332,0.0007330046,0.0006958275,0.0005040207,0.0004738095],"domain_scores_gemma":[0.99661,0.001692839,0.0003163678,0.0002869391,0.0000575277,0.00103629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001415558,0.00005007934,0.000007508946,0.01526543,0.0003246362,0.00006507595,0.00007287441,4.000324e-7,3.95054e-7,0.0006150854,0.0003277727,0.9832566],"study_design_scores_gemma":[0.0009511053,0.0001960285,5.946951e-7,0.005068477,0.001946884,0.0003627864,0.000004979065,0.03131346,0.000001081954,0.0008413038,0.958968,0.0003452831],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.020992e-7,0.6135073,0.3843843,0.0008283174,0.0002549888,0.0005504936,0.000004742065,0.00009694989,0.0003728149],"genre_scores_gemma":[0.000002186726,0.9534328,0.04223717,0.001181141,0.002259247,0.000101637,0.0003711193,0.0001811799,0.0002335012],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9829113,"threshold_uncertainty_score":0.999836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0355448319793312,"score_gpt":0.4180000261170838,"score_spread":0.3824551941377526,"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."}}