{"id":"W2529631604","doi":"10.1038/srep33985","title":"Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"AI in cancer detection","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; National Natural Science Foundation of China; U.S. Department of Defense; Case Comprehensive Cancer Center, Case Western Reserve University; National Institutes of Health; National Cancer Institute; Case Western Reserve University; Cleveland Clinic; Wallace H. Coulter Foundation; Natural Science Basic Research Program of Shaanxi Province","keywords":"Computer science; H&E stain; Artificial intelligence; Voting; Context (archaeology); Digital pathology; Noise (video); Majority rule; Precision and recall; Ground truth; Pattern recognition (psychology); Computer vision; Staining; Pathology; Image (mathematics); Medicine; Biology","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":[0.001922842,0.0001207836,0.0001433753,0.0002214587,0.0002718451,0.0001841957,0.0002807055,0.0000794749,0.000007756046],"category_scores_gemma":[0.0004120242,0.00008787384,0.00008210923,0.0004841319,0.0001736429,0.0007116456,0.0001547595,0.00008116115,0.0000165431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003961115,"about_ca_system_score_gemma":0.00007371313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001870139,"about_ca_topic_score_gemma":0.00009899017,"domain_scores_codex":[0.9978322,0.00006641524,0.000407333,0.001050802,0.0002969597,0.0003462459],"domain_scores_gemma":[0.9987584,0.00009623836,0.000291341,0.0006157928,0.0001708224,0.00006745362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001729098,0.00007579453,0.001994545,0.00001007748,0.000003344028,0.000222414,0.0003057171,0.00006982496,0.6681328,0.0002678,0.0009202071,0.3279802],"study_design_scores_gemma":[0.001173908,0.0004035393,0.04915845,0.0001914009,0.00001282678,0.0009910461,0.00008839268,0.07238436,0.8245441,0.02737758,0.02284611,0.0008283195],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07657422,0.00003748324,0.9136814,0.0002330894,0.008779928,0.0003616298,0.000001056295,0.0002033642,0.0001278271],"genre_scores_gemma":[0.9331457,8.356428e-7,0.06557921,0.00001686783,0.00006447854,0.00009917182,4.183955e-7,0.000009014228,0.001084303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8565715,"threshold_uncertainty_score":0.3583391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03103077541111958,"score_gpt":0.2645292078276323,"score_spread":0.2334984324165127,"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."}}