{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000345037,0.0001100279,0.0001657569,0.0001205074,0.0001877601,0.00001328162,0.00004448989,0.00008922508,0.0005936607],"category_scores_gemma":[0.00001538942,0.0001078435,0.00005301922,0.00009431054,0.000123226,0.00004824137,0.00005448887,0.000111813,0.0003902826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001230584,"about_ca_system_score_gemma":0.00001324094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003658506,"about_ca_topic_score_gemma":0.000005757861,"domain_scores_codex":[0.9992033,0.00006120121,0.0001894248,0.0002304618,0.0001189456,0.0001966861],"domain_scores_gemma":[0.9996534,0.00001289369,0.00006583507,0.0001494989,0.00003715166,0.0000812344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002745774,0.0002507513,0.003733509,0.00001975287,0.00004368189,0.0001780392,0.001287717,0.000007201729,0.9837922,0.0007472212,0.0004232478,0.009242131],"study_design_scores_gemma":[0.001778985,0.0004677745,0.007189721,0.00002553647,0.00003956835,0.007275866,0.004559396,0.1087512,0.8301231,0.00001522864,0.03955625,0.000217457],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845668,0.0001941081,0.004566225,0.0002348618,0.0004793198,0.0002438847,1.860336e-7,0.0001383435,0.009576255],"genre_scores_gemma":[0.9963321,0.000006490482,0.002590245,0.0003693947,0.0001737331,0.0000140382,0.0000127262,0.00001956349,0.0004817125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1536691,"threshold_uncertainty_score":0.6500169,"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."}}