{"id":"W2323638464","doi":"10.1109/embc.2014.6944883","title":"Efficient epidermis segmentation for whole slide skin histopathological images","year":2014,"lang":"en","type":"article","venue":"","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Thresholding; Segmentation; Artificial intelligence; Computer science; Image segmentation; Computer vision; Pattern recognition (psychology); Matching (statistics); Template matching; Computer-aided diagnosis; Image (mathematics); Epidermis (zoology); Mathematics","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":[0.0003897261,0.0003822584,0.0004150178,0.001283644,0.0002659555,0.0005801183,0.0005656813,0.000620331,0.002142962],"category_scores_gemma":[0.0007855061,0.0003987276,0.0004543049,0.0007256434,0.0001989775,0.0005832643,0.0003553866,0.000352784,0.001331363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000240257,"about_ca_system_score_gemma":0.0004611533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001057016,"about_ca_topic_score_gemma":0.002059873,"domain_scores_codex":[0.9996836,0.00003834699,0.00001953569,0.00007817132,0.000150831,0.0000294159],"domain_scores_gemma":[0.9996532,0.0001005303,0.00004420987,0.00006495698,0.000120381,0.00001676359],"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":[0.0001413306,0.00003321926,0.001494645,0.0001899883,0.0000289835,0.0002989895,0.000089617,0.003959692,0.7744473,0.0005535711,0.0009533642,0.2178093],"study_design_scores_gemma":[0.00003136343,0.000224203,0.01725717,0.00004817536,0.00009896732,0.004133927,0.0001701273,0.2399721,0.7267405,0.001456045,0.009803387,0.00006403768],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0614273,0.0008247934,0.9335681,0.00008853253,0.00004103433,0.0001024638,0.0001194553,0.002574323,0.001254041],"genre_scores_gemma":[0.2241528,0.0007949934,0.7720152,0.00006658334,0.00002589818,0.00006542789,0.0002478684,0.0002796093,0.00235169],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002142962,"threshold_uncertainty_score":0.007168949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468053688805277,"score_gpt":0.2718540883406634,"score_spread":0.2571735514526106,"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."}}