{"id":"W3009334705","doi":"10.3390/cancers12030578","title":"Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Digital pathology; Gray level; Glioma; Grading (engineering); Artificial intelligence; Medicine; Random forest; Digital image analysis; Pathology; Atypia; Support vector machine; Computer science; Computer vision; Cancer research; Biology","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":[0.001055376,0.0005337425,0.0004301589,0.003631006,0.0001332548,0.0007731476,0.0003247562,0.0003502517,0.0009636608],"category_scores_gemma":[0.003047053,0.0001381097,0.00045755,0.001107441,0.0002610942,0.0004313783,0.0004106878,0.0002886585,0.0004258604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003345085,"about_ca_system_score_gemma":0.0002877819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001893752,"about_ca_topic_score_gemma":0.002281827,"domain_scores_codex":[0.9994893,0.0001004964,0.00006309918,0.0001220807,0.0001823792,0.00004247926],"domain_scores_gemma":[0.9989447,0.0003652457,0.0002521411,0.0001001558,0.0002957481,0.00004203811],"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":[0.001239789,0.0003961455,0.1322969,0.0004879419,0.0003662804,0.0004876574,0.0001861617,0.0374233,0.09108806,0.0006277226,0.002283549,0.7331166],"study_design_scores_gemma":[0.00004809405,0.0006329931,0.209407,0.00006313264,0.0002253112,0.001479717,0.0002124395,0.7336981,0.05064191,0.001511452,0.001999094,0.00008081052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8014128,0.0007799889,0.1908307,0.0001350265,0.0000581988,0.0004711592,0.001477606,0.002634509,0.002199973],"genre_scores_gemma":[0.9199997,0.0002588787,0.07809544,0.0000245734,0.00002682555,0.0001085899,0.0009417767,0.00002539942,0.0005187967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003631006,"threshold_uncertainty_score":0.005581439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01852469626171952,"score_gpt":0.2985756986660744,"score_spread":0.2800510024043549,"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."}}