{"id":"W2051106546","doi":"10.1109/embc.2012.6346956","title":"Extracting morphological high-level intuitive features (HLIF) for enhancing skin lesion classification","year":2012,"lang":"en","type":"article","venue":"","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Feature extraction; Set (abstract data type); Skin lesion; Computer vision","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":[0.0007694953,0.0006810111,0.0004783875,0.002368365,0.0001860664,0.0006478093,0.0004070375,0.0007099731,0.001470632],"category_scores_gemma":[0.002400049,0.0002247528,0.0005511596,0.001043312,0.0003254855,0.0008772083,0.0004674604,0.0004504173,0.0007691361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001962081,"about_ca_system_score_gemma":0.0003060043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005023805,"about_ca_topic_score_gemma":0.001071352,"domain_scores_codex":[0.9996483,0.00005579257,0.00003330027,0.00006107184,0.0001472603,0.00005429356],"domain_scores_gemma":[0.9988513,0.0004534914,0.0002062952,0.0001381537,0.0002818809,0.00006885546],"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":[0.0002368076,0.0001807104,0.0100446,0.0004263757,0.00005453425,0.0003525281,0.0001030624,0.005151088,0.2894253,0.0008627338,0.002102158,0.6910601],"study_design_scores_gemma":[0.00008518301,0.001488806,0.1810705,0.000216871,0.0003499161,0.006249868,0.0003685478,0.3356336,0.4517039,0.007619803,0.01497924,0.0002338307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1921208,0.0007893723,0.8012578,0.0001771673,0.0000596358,0.0002899215,0.0005714977,0.002703844,0.002029923],"genre_scores_gemma":[0.4936159,0.0004399489,0.5038314,0.00009544294,0.00006256264,0.000122658,0.0008437611,0.0001216422,0.0008666579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002368365,"threshold_uncertainty_score":0.004919708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0917907499056919,"score_gpt":0.3325859866482439,"score_spread":0.240795236742552,"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."}}