{"id":"W4386838320","doi":"10.18280/ria.370419","title":"ScaledDenseNet: An Efficient Deep Learning Architecture for Skin Lesion Identification","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Architecture; Deep learning; Lesion; Artificial intelligence; Computer science; Skin lesion; Computer architecture; Medicine; Dermatology; Pathology; Biology; Art; Visual arts","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004584132,0.001064601,0.0005235266,0.0007223619,0.0002665394,0.0006488056,0.001739583,0.0007306586,0.0035245],"category_scores_gemma":[0.00101921,0.000385752,0.0005278685,0.0004878202,0.0003231505,0.001082634,0.001094046,0.0007979665,0.001321299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007403402,"about_ca_system_score_gemma":0.0008850637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005914152,"about_ca_topic_score_gemma":0.01423865,"domain_scores_codex":[0.9998118,0.00002577813,0.000008306229,0.00006375428,0.00005687738,0.000033463],"domain_scores_gemma":[0.9997794,0.00005501841,0.00002173085,0.0000442106,0.0000770987,0.00002263529],"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":[0.0002388673,0.0002520757,0.006289537,0.0003106631,0.0002109109,0.0003806699,0.00009547056,0.3023896,0.02581642,0.009442518,0.02238395,0.6321893],"study_design_scores_gemma":[0.00001205193,0.0001003703,0.0007948047,0.00003315372,0.00003141447,0.0001539074,0.00001869541,0.9804361,0.008210699,0.004282733,0.005910091,0.000015938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09464157,0.002526921,0.8792346,0.0005012037,0.0003185759,0.0002305329,0.001523665,0.01020051,0.01082253],"genre_scores_gemma":[0.6445286,0.001160254,0.3332531,0.0007867315,0.0000824723,0.0002821849,0.005560042,0.0004362849,0.01391041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005914152,"threshold_uncertainty_score":0.01179069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03813275543700268,"score_gpt":0.3054998972504478,"score_spread":0.2673671418134451,"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."}}