{"id":"W2296596199","doi":"10.1049/iet-ipr.2014.0192","title":"Automated segmentation of the epidermis area in skin whole slide histopathological images","year":2015,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Epidermis (zoology); Segmentation; Computer science; Artificial intelligence; Image segmentation; Computer vision; Anatomy; 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":[0.000575457,0.0001140216,0.0001576182,0.0001202174,0.00008087029,0.0001423476,0.0004812977,0.00005627307,0.000002677278],"category_scores_gemma":[0.0002241709,0.00008234467,0.0000478572,0.0006044917,0.0001019434,0.0009830907,0.000176299,0.0001454985,0.000006763176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001094216,"about_ca_system_score_gemma":0.0001212742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000472925,"about_ca_topic_score_gemma":0.000005836194,"domain_scores_codex":[0.9988683,0.0001362784,0.0003008603,0.0002548526,0.0002489015,0.0001908297],"domain_scores_gemma":[0.9992711,0.00002977518,0.0001942115,0.0002777535,0.0001864691,0.00004072381],"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":[0.00002498006,0.0002550481,0.002772773,0.0001491036,0.00000454371,0.0001225809,0.005995469,0.0001094791,0.4709482,0.00002909022,0.008097812,0.5114909],"study_design_scores_gemma":[0.000296033,0.00005011378,0.005884399,0.00008344005,0.000004135228,0.00006858975,0.0002336235,0.02976253,0.9609472,0.00235049,0.000182747,0.0001367414],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06656878,0.0004147517,0.9273849,0.001143241,0.000191342,0.0002989342,0.000002921869,0.001075827,0.002919265],"genre_scores_gemma":[0.88614,0.00000294128,0.1134067,0.0002603209,0.00001821637,0.00002487439,0.00000111618,0.000007612529,0.0001381896],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8195713,"threshold_uncertainty_score":0.3357918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02246556169146766,"score_gpt":0.2855079294553222,"score_spread":0.2630423677638546,"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."}}