{"id":"W2036099935","doi":"10.1007/s11517-012-1009-2","title":"Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns","year":2012,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Segmentation; Standard deviation; Pattern recognition (psychology); Binary number; Artificial intelligence; Invariant (physics); Local binary patterns; Root mean square; Hausdorff distance; Boundary (topology); Rotation (mathematics); Mathematics; Image segmentation; Computer vision; Computer science; Algorithm; Biology; Image (mathematics); Physics; Histogram; Mathematical analysis; Statistics","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":[0.0002773966,0.0002570469,0.0003274572,0.0009028797,0.0002330216,0.0006555408,0.0004241426,0.0005359989,0.0004159126],"category_scores_gemma":[0.0007577568,0.0002632037,0.0002811157,0.0004470226,0.0003088438,0.0002837643,0.0002202312,0.0003094533,0.0001507785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004085205,"about_ca_system_score_gemma":0.0006217296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004006644,"about_ca_topic_score_gemma":0.004632826,"domain_scores_codex":[0.9998634,0.00002660933,0.000005720898,0.0000418323,0.00004173396,0.00002072307],"domain_scores_gemma":[0.9997193,0.0001194635,0.00005026846,0.00002255915,0.00007208189,0.00001625435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004877584,0.0001782003,0.006193593,0.000159138,0.00003785493,0.0001823618,0.0002174674,0.06878014,0.6060728,0.002281302,0.0007484287,0.314661],"study_design_scores_gemma":[0.00002741086,0.00009299704,0.01034265,0.00001833113,0.00003133749,0.0002156681,0.00008340042,0.8687752,0.1179203,0.001513842,0.0009589727,0.00001982022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5038487,0.0004066738,0.4926728,0.0001956225,0.00002380875,0.00009648458,0.0001294739,0.000935137,0.00169137],"genre_scores_gemma":[0.6551324,0.0001707618,0.3428491,0.00005474304,0.000009516834,0.00006224836,0.0001950648,0.0001274371,0.001398751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004006644,"threshold_uncertainty_score":0.007966638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494769387221235,"score_gpt":0.2518443385284673,"score_spread":0.236896644656255,"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."}}