{"id":"W4377023543","doi":"10.1016/j.dib.2023.109249","title":"A skin lesion hair mask dataset with fine-grained annotations","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Hair Growth and Disorders","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"European Regional Development Fund; Mutualité Sociale Agricole; Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture; Institut National de la Recherche Agronomique","keywords":"Computer science; Segmentation; Artificial intelligence; Annotation; Skin lesion; Benchmarking; Pattern recognition (psychology); Process (computing); Computer vision; Dermatology; Medicine","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":[0.0007021751,0.001375375,0.0007524754,0.001837021,0.0006259015,0.0007841834,0.001285317,0.001589855,0.005184005],"category_scores_gemma":[0.001956272,0.0004020801,0.0009382403,0.0008844955,0.0004790718,0.0005749955,0.001303399,0.0009526426,0.004282962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006315091,"about_ca_system_score_gemma":0.0005838207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004449689,"about_ca_topic_score_gemma":0.0149582,"domain_scores_codex":[0.9991674,0.00009894779,0.00007513165,0.0003000704,0.000266651,0.00009168366],"domain_scores_gemma":[0.998848,0.00028096,0.00009146191,0.0003826554,0.0002920555,0.0001048432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002120318,0.001346911,0.02568186,0.005365318,0.0006079716,0.003812957,0.0007139706,0.01382307,0.1434138,0.001693631,0.4844615,0.3169587],"study_design_scores_gemma":[0.0006117459,0.001458063,0.2094115,0.001275417,0.0005246521,0.02507839,0.00129496,0.1084038,0.1546168,0.005624936,0.4913012,0.0003984873],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.3827573,0.008953827,0.07804059,0.001340373,0.001048906,0.002317188,0.4796479,0.02858468,0.0173093],"genre_scores_gemma":[0.2351109,0.001263491,0.07418218,0.0006550279,0.0001371367,0.001178311,0.6797531,0.00122478,0.006494952],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.005184005,"threshold_uncertainty_score":0.01734221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0581635138978258,"score_gpt":0.3353147200357616,"score_spread":0.2771512061379358,"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."}}