{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005627906,0.0007761292,0.0007038635,0.001924857,0.0002827877,0.001041778,0.001282409,0.0008309773,0.004086554],"category_scores_gemma":[0.001636612,0.0004023404,0.0006769623,0.0006756004,0.0001746367,0.000784322,0.0006587082,0.0007118779,0.001821402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000526781,"about_ca_system_score_gemma":0.000622235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002803969,"about_ca_topic_score_gemma":0.005530201,"domain_scores_codex":[0.9996883,0.00003110377,0.00001982816,0.0001008302,0.0001261177,0.00003396946],"domain_scores_gemma":[0.9996049,0.000126565,0.00006119115,0.00004819333,0.000124097,0.00003505434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005180677,0.0001457053,0.008032627,0.0004865722,0.0002415265,0.0003823777,0.0001099242,0.01772805,0.1002265,0.001464028,0.02635203,0.8443127],"study_design_scores_gemma":[0.00008347741,0.0003255752,0.01288404,0.00006842073,0.0001251358,0.001832318,0.00009624699,0.8136191,0.1375568,0.0032807,0.03001554,0.000112632],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0391994,0.001392865,0.8874574,0.0002223281,0.0001582036,0.0002999495,0.002261772,0.06627148,0.002736637],"genre_scores_gemma":[0.2663775,0.001053416,0.7151604,0.000421664,0.0001618676,0.0006424916,0.006314431,0.002000291,0.007868017],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004086554,"threshold_uncertainty_score":0.01367086,"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."}}