{"id":"W4231958199","doi":"10.32920/ryerson.14649345","title":"Skin Lesion Segmentation Techniques for Melanoma Diagnosis: Comparative Studies","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Segmentation; Cluster analysis; Pattern recognition (psychology); Computer science; Image segmentation; Particle swarm optimization; Skin cancer; Scale-space segmentation; Segmentation-based object categorization; Region growing; Support vector machine; Cancer; Medicine; Machine learning","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":[0.004300206,0.0007229112,0.0008197469,0.004226503,0.00044946,0.0009235259,0.0005635225,0.001127963,0.001814797],"category_scores_gemma":[0.01001395,0.0002906243,0.001200265,0.002007188,0.0003890069,0.0007885867,0.0003815401,0.0003134717,0.000588753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004516594,"about_ca_system_score_gemma":0.0003102796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002214765,"about_ca_topic_score_gemma":0.002124626,"domain_scores_codex":[0.9970392,0.001082892,0.0002650358,0.0005705267,0.0009301897,0.0001122263],"domain_scores_gemma":[0.9920522,0.004835268,0.0003666424,0.0005203406,0.00206822,0.0001574199],"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":[0.005883442,0.0009104413,0.05227598,0.004743807,0.001911958,0.000686295,0.001002553,0.01234947,0.07923999,0.0008337809,0.002147864,0.8380144],"study_design_scores_gemma":[0.000501109,0.01503827,0.4816654,0.001860273,0.007812898,0.01841448,0.003353023,0.1580583,0.2693097,0.003549197,0.03997241,0.0004649592],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7769951,0.119604,0.08870316,0.0004728032,0.0004659985,0.0006026124,0.0009707182,0.0007362837,0.0114494],"genre_scores_gemma":[0.9191096,0.0217332,0.05540702,0.0001498904,0.0002447751,0.00009805802,0.001179675,0.0002059431,0.001871835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004300206,"threshold_uncertainty_score":0.02274191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1124631490301873,"score_gpt":0.3965278800648023,"score_spread":0.284064731034615,"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."}}