{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002414222,0.001047432,0.0009698652,0.001486263,0.0004051441,0.00231848,0.001607938,0.002253947,0.005086423],"category_scores_gemma":[0.008116109,0.0004643978,0.001186038,0.001513565,0.001933261,0.002361925,0.002030646,0.004732956,0.002136185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001580814,"about_ca_system_score_gemma":0.001418336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003264945,"about_ca_topic_score_gemma":0.002283322,"domain_scores_codex":[0.998941,0.0004043697,0.00006866451,0.0001914625,0.0003335456,0.00006091658],"domain_scores_gemma":[0.9978184,0.001394472,0.000149174,0.0002381853,0.0003399903,0.00005982295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006731851,0.00004278482,0.0007190518,0.0006019224,0.000126847,0.0001428423,0.0001241325,0.2131644,0.001754468,0.4970236,0.02562231,0.2606102],"study_design_scores_gemma":[0.00001325422,0.00002828643,0.0002623538,0.0001509324,0.00002195943,0.0001236304,0.00002204651,0.6246159,0.0008697428,0.3348093,0.03905229,0.0000302256],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0009899046,0.009759481,0.979882,0.002741598,0.0003691386,0.00002965276,0.0001830814,0.0004722934,0.005572884],"genre_scores_gemma":[0.2021957,0.03665596,0.7246031,0.002909915,0.003257133,0.0005929496,0.001370939,0.0008936668,0.02752061],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005086423,"threshold_uncertainty_score":0.01701576,"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."}}