{"id":"W4415283044","doi":"10.1038/s41598-025-20275-4","title":"A probabilistic detection-based approach to skin and freckle segmentation","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Division of Human Resource Development; Korea Evaluation Institute of Industrial Technology; Ministry of Science and ICT, South Korea; Korea Health Industry Development Institute; National Research Foundation of Korea; Institute for Information and Communications Technology Promotion; National IT Industry Promotion Agency; Ministry of Trade, Industry and Energy; National Research Foundation","keywords":"Segmentation; Pattern recognition (psychology); Probabilistic logic; Image segmentation; Histogram; Region growing; Scale-space segmentation; Process (computing)","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":[0.001734828,0.001129413,0.001247529,0.004600707,0.001077695,0.001921291,0.002732516,0.001965237,0.002500583],"category_scores_gemma":[0.002928465,0.001056092,0.00187486,0.002263406,0.001254684,0.001845341,0.002082012,0.001427687,0.001848147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247223,"about_ca_system_score_gemma":0.001820886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005199421,"about_ca_topic_score_gemma":0.007297297,"domain_scores_codex":[0.9978861,0.0003099527,0.0001191412,0.0006495768,0.0008210473,0.0002141292],"domain_scores_gemma":[0.9987362,0.0003279795,0.0001966008,0.0002070515,0.0004750057,0.00005711831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001875458,0.0001809813,0.003757869,0.0004300891,0.000217213,0.0004235153,0.0003714891,0.1826548,0.09003527,0.03337553,0.005392548,0.6829731],"study_design_scores_gemma":[0.00001000429,0.0001080797,0.003430491,0.00006220892,0.0000764996,0.001173842,0.00009561403,0.9319521,0.03184052,0.01796343,0.01318402,0.0001031545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001508892,0.0001693679,0.997155,0.00005302358,0.00001528295,0.00004587933,0.0000278441,0.0004343741,0.0005904579],"genre_scores_gemma":[0.1211778,0.0009086148,0.8718906,0.0002219027,0.0001246479,0.0002036861,0.0002966226,0.0004131617,0.004762783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005199421,"threshold_uncertainty_score":0.01033837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01288076935658215,"score_gpt":0.2492460782114828,"score_spread":0.2363653088549007,"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."}}