{"id":"W3115970425","doi":"10.3390/cancers13010011","title":"piNET–An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images","year":2020,"lang":"en","type":"article","venue":"Cancers","topic":"AI in cancer detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; University of Guelph; Toronto Metropolitan University","funders":"","keywords":"False positive paradox; Computer science; Artificial intelligence; Workflow; Pattern recognition (psychology); Segmentation; Proliferation index; Pathology; Medicine; Database","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":[0.0000929304,0.0001876188,0.0001921797,0.00005360213,0.0001775875,0.0001922747,0.0005361313,0.0001373009,0.00004286056],"category_scores_gemma":[0.00004300741,0.0001900075,0.00006632504,0.0005404321,0.00005267629,0.000814645,0.00006929252,0.0001867973,0.00001192204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218832,"about_ca_system_score_gemma":0.0008913552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003078946,"about_ca_topic_score_gemma":0.00005511927,"domain_scores_codex":[0.9985416,0.00004115314,0.0002268434,0.0005900753,0.0002661398,0.0003342242],"domain_scores_gemma":[0.9990206,0.00003748783,0.0001414559,0.0003653611,0.0001952378,0.0002398073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009498188,0.00007515835,0.01500335,0.0007478282,0.0004128341,0.00002194135,0.0104886,0.4848111,0.01951339,0.02737167,0.1013955,0.3392088],"study_design_scores_gemma":[0.0004628246,0.0001511749,0.005982694,0.00004918892,0.00001422864,0.000006808099,0.00003958736,0.9816165,0.006684758,0.001052136,0.003645357,0.0002947583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01255968,0.0001929218,0.976251,0.007442247,0.00129265,0.0006443507,0.00008466921,0.001481161,0.00005132193],"genre_scores_gemma":[0.9648402,0.00003063051,0.02943365,0.003935129,0.001173713,0.0005198234,0.000007979065,0.00003476711,0.00002412134],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9522805,"threshold_uncertainty_score":0.774828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02033877341253923,"score_gpt":0.3078196422032692,"score_spread":0.28748086879073,"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."}}