{"id":"W4300177118","doi":"10.1007/978-3-030-00928-1","title":"Medical Image Computing and Computer Assisted Intervention – MICCAI 2018","year":2018,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Image quality; Artificial intelligence; Computer vision; Medical physics; Medical imaging; Image processing; Image (mathematics); Data science; Medicine","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"],"consensus_categories":[],"category_scores_codex":[0.002334185,0.0004388129,0.0007820093,0.0006250512,0.0002302121,0.000276094,0.0006826086,0.0004374268,0.0001205173],"category_scores_gemma":[0.0007050235,0.0003675221,0.0001636829,0.0004370062,0.002356711,0.0001500537,0.0009494442,0.001787604,0.00003468136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000358149,"about_ca_system_score_gemma":0.0007202379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000429604,"about_ca_topic_score_gemma":0.00001938014,"domain_scores_codex":[0.9961292,0.0001156104,0.000678028,0.001178567,0.001270763,0.0006278175],"domain_scores_gemma":[0.9979014,0.0005632051,0.000287549,0.0005506522,0.0002290075,0.0004682165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002556259,0.0001056681,0.0008971872,0.0003689157,0.00004234056,0.0004070229,0.00041711,0.00009105732,0.000129717,0.00004415167,0.0045003,0.9929709],"study_design_scores_gemma":[0.001539662,0.0005061781,0.004979487,0.00452146,0.00005792333,0.00155304,4.218199e-7,0.9791498,0.00009575469,0.001971494,0.00517873,0.0004460969],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005614013,0.0004787898,0.9875262,0.002286631,0.002467694,0.0003419805,0.000002421268,0.000126508,0.001155791],"genre_scores_gemma":[0.1092862,0.0001229354,0.8652561,0.01323182,0.01022711,0.000006861194,0.0001407051,0.0001592296,0.001568928],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9925249,"threshold_uncertainty_score":0.9998777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00976282647887128,"score_gpt":0.3047089176856955,"score_spread":0.2949460912068242,"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."}}