{"id":"W2972222924","doi":"10.3389/fbioe.2019.00226","title":"IHC Color Histograms for Unsupervised Ki67 Proliferation Index Calculation","year":2019,"lang":"en","type":"article","venue":"Frontiers in Bioengineering and Biotechnology","topic":"AI in cancer detection","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"Ryerson University","keywords":"Digital pathology; Computer science; Artificial intelligence; Ground truth; Histogram; Histogram equalization; Pattern recognition (psychology); Proliferation index; Proliferative index; Computer vision; Pathology; Image (mathematics); Medicine; Immunohistochemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009956354,0.0005898631,0.0004394069,0.002317235,0.0003329593,0.0009689323,0.0009718626,0.0003673433,0.002522392],"category_scores_gemma":[0.002213463,0.0003517847,0.0006754639,0.001275361,0.0003520175,0.0008189565,0.0008268459,0.000840446,0.001883738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007468228,"about_ca_system_score_gemma":0.00112501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003219893,"about_ca_topic_score_gemma":0.006446563,"domain_scores_codex":[0.9992138,0.00009111055,0.00003557143,0.00018424,0.000389174,0.00008610146],"domain_scores_gemma":[0.9985526,0.0002569681,0.0002213056,0.0002430022,0.0006706333,0.00005550535],"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":[0.0003181067,0.0002411256,0.009983913,0.0002823278,0.0001490922,0.00008748394,0.0001882329,0.02982136,0.2512905,0.006093026,0.00685661,0.6946881],"study_design_scores_gemma":[0.00003405575,0.0001764059,0.02831872,0.00003006203,0.00006591071,0.0004076981,0.0001460221,0.6841732,0.2670931,0.005402588,0.01400661,0.0001456906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03035104,0.0001994651,0.9624771,0.0000391733,0.0000372988,0.0001163433,0.000425966,0.00474916,0.001604402],"genre_scores_gemma":[0.2762761,0.000334344,0.7170053,0.00008175768,0.00004631462,0.0002495556,0.002101518,0.0007182946,0.003186828],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003219893,"threshold_uncertainty_score":0.00843817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005247886707092191,"score_gpt":0.1960893424726825,"score_spread":0.1908414557655903,"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."}}