{"id":"W4302362817","doi":"10.32920/21287937.v1","title":"IHC Color Histograms for Unsupervised Ki67 Proliferation Index Calculation","year":2022,"lang":"en","type":"preprint","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Digital pathology; Artificial intelligence; Histogram; Ground truth; Computer science; Histogram equalization; Proliferation index; Pattern recognition (psychology); Workload; Computer vision; Pathology; Image (mathematics); Medicine; Immunohistochemistry","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":[0.0008212919,0.0006625165,0.0004745577,0.002624894,0.0003641595,0.0009810162,0.0009486236,0.0003588582,0.00280151],"category_scores_gemma":[0.002071713,0.0003428807,0.0006743923,0.001574634,0.0003329828,0.0008140354,0.0008374589,0.0009168612,0.002527681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006486548,"about_ca_system_score_gemma":0.001192792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004214101,"about_ca_topic_score_gemma":0.00846793,"domain_scores_codex":[0.9991565,0.00008357886,0.00003804214,0.0002111528,0.0004040178,0.0001067712],"domain_scores_gemma":[0.9985922,0.0002035127,0.0002012576,0.000238041,0.0007103421,0.0000546621],"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":[0.0002153219,0.0001991493,0.008273665,0.0002432705,0.000120308,0.0000788969,0.0001544633,0.02001773,0.1591439,0.004416503,0.01013474,0.797002],"study_design_scores_gemma":[0.00003403249,0.0001785843,0.0319959,0.00003447394,0.00008174347,0.0004599643,0.0002038583,0.702791,0.2346212,0.006736509,0.02271484,0.0001478443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02982241,0.0002903942,0.9602194,0.00005236663,0.00005682722,0.0001488164,0.0006643558,0.006409366,0.002336044],"genre_scores_gemma":[0.3279336,0.0005015734,0.659807,0.0001145772,0.0000785614,0.0003393162,0.004089807,0.001031938,0.006103688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004214101,"threshold_uncertainty_score":0.009371996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03187200794147423,"score_gpt":0.2869350455357409,"score_spread":0.2550630375942667,"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."}}