{"id":"W2783283661","doi":"10.1007/s10916-017-0885-2","title":"A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning","year":2018,"lang":"en","type":"article","venue":"Journal of Medical Systems","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Classifier (UML); Computer-aided diagnosis; Feature (linguistics); Pattern recognition (psychology); Generalizability theory; Computer vision; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005253051,0.0006954177,0.0006869697,0.0007908322,0.0002844854,0.0006199421,0.00109686,0.001121188,0.003343371],"category_scores_gemma":[0.001132233,0.0003465863,0.0004642026,0.0003550979,0.0001339429,0.0004716615,0.0008268614,0.0007883429,0.001148484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005405711,"about_ca_system_score_gemma":0.0007539382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005069006,"about_ca_topic_score_gemma":0.007997654,"domain_scores_codex":[0.999785,0.00002281715,0.00001785871,0.00008211568,0.00006165772,0.00003058479],"domain_scores_gemma":[0.9996222,0.0001448692,0.00003472478,0.00002836176,0.0001269141,0.00004287373],"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":[0.001091655,0.0005963651,0.00602322,0.0002142329,0.0001923903,0.0006214471,0.00007006164,0.02216492,0.05391767,0.000556516,0.0154961,0.8990554],"study_design_scores_gemma":[0.00008397675,0.0002795053,0.004173044,0.0000300034,0.00007248036,0.0004361128,0.00002537566,0.964735,0.02686746,0.0009862448,0.002273476,0.00003741584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1245901,0.001353216,0.8442748,0.000925295,0.0004342682,0.0004142199,0.00180151,0.02423894,0.001967605],"genre_scores_gemma":[0.6445113,0.0005236934,0.3459057,0.000941026,0.0001546656,0.0004121932,0.001720126,0.0001914688,0.005639763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005069006,"threshold_uncertainty_score":0.01118469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006443515188478098,"score_gpt":0.2596845432954565,"score_spread":0.2532410281069785,"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."}}