{"id":"W2892053105","doi":"10.1111/exd.13777","title":"Multimodal skin lesion classification using deep learning","year":2018,"lang":"en","type":"article","venue":"Experimental Dermatology","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":317,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; MetaOptima Technology (Canada)","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Pattern recognition (psychology); Binary classification; Skin lesion; Classifier (UML); Contextual image classification; Multiclass classification; Metadata; Binary number; Lesion; Image (mathematics); Medicine; Dermatology; Mathematics; Support vector machine; Pathology","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":[0.0008300344,0.0007664071,0.0005602308,0.001827546,0.0001855692,0.00060824,0.0005860134,0.0006320999,0.001970946],"category_scores_gemma":[0.001196579,0.0002038971,0.0005543813,0.0007511965,0.0001932297,0.000645385,0.0006974593,0.0004756067,0.0007311253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000646252,"about_ca_system_score_gemma":0.0004316751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003993606,"about_ca_topic_score_gemma":0.005859531,"domain_scores_codex":[0.9995762,0.00008404151,0.00002898199,0.0001161199,0.0001210747,0.00007361672],"domain_scores_gemma":[0.9995853,0.0001233083,0.00007380256,0.00006076312,0.0001246941,0.00003226058],"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":[0.0004438749,0.0004940918,0.02309103,0.0001742935,0.0002679788,0.0004293736,0.0000682251,0.1149232,0.04643065,0.00096008,0.005696405,0.8070208],"study_design_scores_gemma":[0.00001032633,0.00009138588,0.00592517,0.00001724976,0.00003770376,0.000204363,0.00003452834,0.9776611,0.01369174,0.001342908,0.0009687386,0.00001476472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4724834,0.002191895,0.5137001,0.0006286035,0.0001285937,0.0002480707,0.001436846,0.004488783,0.004693796],"genre_scores_gemma":[0.9140692,0.0002666037,0.08130919,0.0001559013,0.00006196473,0.00007256538,0.001347275,0.00005139104,0.002665841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003993606,"threshold_uncertainty_score":0.00794071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03597828530170585,"score_gpt":0.3300928749924396,"score_spread":0.2941145896907337,"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."}}