{"id":"W2807026579","doi":"10.1109/isbi.2018.8363712","title":"Generative adversarial networks to segment skin lesions","year":2018,"lang":"en","type":"article","venue":"","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Segmentation; Artificial intelligence; Convolutional neural network; Computer science; Pattern recognition (psychology); Skin lesion; Deep learning; Generative grammar; Image segmentation; Complement (music); Generative adversarial network; Adversarial system; Computer vision; Medicine","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006588581,0.0000790201,0.0001063548,0.00006839984,0.00009622297,0.00001419667,0.00003492683,0.00003698461,0.002900835],"category_scores_gemma":[0.00001631712,0.00006186793,0.00004467164,0.0001348152,0.00002465654,0.00001446254,0.00006119263,0.00005519705,0.0006753406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007391295,"about_ca_system_score_gemma":0.00001896143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000063754,"about_ca_topic_score_gemma":0.000206315,"domain_scores_codex":[0.999406,0.00001578703,0.0001133863,0.0001782982,0.0001281372,0.0001583734],"domain_scores_gemma":[0.9995708,0.00001047951,0.00001565508,0.0001799149,0.00006041863,0.0001627007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001068436,0.0003736459,0.0002877467,0.00001219107,0.0002933117,0.0001655217,0.001310349,0.0007876315,0.007172126,0.006747491,0.7417953,0.2399863],"study_design_scores_gemma":[0.001729437,0.002183508,0.003258098,0.00002939174,0.0001108978,0.0001230296,0.0007236757,0.01377164,0.01349489,0.00004473718,0.9642941,0.0002365514],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1358034,0.00002253782,0.5365675,0.008182108,0.003145675,0.001463683,0.000002407278,0.0003177426,0.3144949],"genre_scores_gemma":[0.9282086,0.000007218561,0.00704204,0.007396622,0.001872995,0.00002243763,0.000005589499,0.00001156564,0.05543296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7924051,"threshold_uncertainty_score":0.9980106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01650159710086042,"score_gpt":0.2712979566673373,"score_spread":0.2547963595664769,"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."}}