{"id":"W3101763508","doi":"10.1101/2020.11.19.390401","title":"piNET: An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"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; Segmentation; Pattern recognition (psychology); Proliferation index; Calculator; Medicine; Pathology","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":[0.0006360799,0.0008287071,0.0007725143,0.002041496,0.0002661899,0.001116258,0.001499294,0.0008761453,0.004909057],"category_scores_gemma":[0.001748394,0.0004360506,0.0007110701,0.0007220918,0.0001935358,0.0008266207,0.0007718219,0.000748901,0.002114048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005805126,"about_ca_system_score_gemma":0.0006166685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002893524,"about_ca_topic_score_gemma":0.004938262,"domain_scores_codex":[0.9996541,0.00003724413,0.00002173828,0.0001121626,0.0001383414,0.00003650309],"domain_scores_gemma":[0.9995684,0.000140638,0.00006220966,0.00005639632,0.0001309155,0.00004145796],"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":[0.000623726,0.0001560416,0.006247756,0.0004951012,0.0002702413,0.0003800345,0.0001108304,0.02314025,0.09277976,0.001732922,0.03673566,0.8373277],"study_design_scores_gemma":[0.00007859664,0.0002108392,0.007082287,0.00004566576,0.00008225538,0.0009409035,0.00006149919,0.8699276,0.09787271,0.003088002,0.0205293,0.00008031423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03236992,0.001103419,0.8735407,0.0002280248,0.0001531028,0.0002634417,0.002403764,0.08799765,0.001939952],"genre_scores_gemma":[0.2604573,0.0008065855,0.7213107,0.0003966989,0.0001688595,0.0005885002,0.00652345,0.002521017,0.007226881],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004909057,"threshold_uncertainty_score":0.01642245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769872933523995,"score_gpt":0.2732528424412952,"score_spread":0.2555541131060552,"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."}}