{"id":"W2970954726","doi":"10.1016/j.compbiomed.2019.103420","title":"Automated segmentation of villi in histopathology images of placenta","year":2019,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; Children's Hospital of Eastern Ontario; University of Ottawa; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"Segmentation; Placenta; Chorionic villi; Artificial intelligence; Pipeline (software); Pattern recognition (psychology); H&E stain; Computer science; Pathology; Computer vision; Medicine; Biology; Staining; Pregnancy; Fetus","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002162022,0.00007077213,0.0004200188,0.0001799737,0.000006522796,1.430082e-7,0.0000385108,0.00008017889,0.00001845576],"category_scores_gemma":[0.00003803943,0.00005262376,0.00001345184,0.0001066066,0.0003175309,0.00001599814,0.00003600832,0.00008723832,7.422669e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001827623,"about_ca_system_score_gemma":0.00001472182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005675002,"about_ca_topic_score_gemma":0.00001164242,"domain_scores_codex":[0.9993834,0.00007229461,0.0002732934,0.0001390224,0.00003315401,0.00009880038],"domain_scores_gemma":[0.9996099,0.000166181,0.00008721478,0.00009808353,0.00002047168,0.00001818876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002249918,0.00005601079,0.9366277,0.0002486328,0.00001586135,0.0000132821,0.001395745,0.000004103089,0.05671496,0.0006342685,0.0003374891,0.003726959],"study_design_scores_gemma":[0.004674032,0.0009783924,0.988851,0.0009660978,0.00002191511,0.00004106057,0.000302456,0.0005729443,0.002869569,0.0005425795,0.0001278774,0.00005214027],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915758,0.00549803,0.0002433773,0.0009101047,0.0003043222,0.0002363414,0.000002221473,0.00001830838,0.001211519],"genre_scores_gemma":[0.9972471,0.0009936423,0.001400147,0.000289192,0.00001634956,0.000003913864,0.0000232829,0.000002688768,0.00002369843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05384539,"threshold_uncertainty_score":0.2145935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415370222346613,"score_gpt":0.3222725653245652,"score_spread":0.3081188631010991,"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."}}