{"id":"W2766325998","doi":"10.1111/srt.12422","title":"A feature fusion system for basal cell carcinoma detection through data‐driven feature learning and patient profile","year":2017,"lang":"en","type":"article","venue":"Skin Research and Technology","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Autoencoder; Pattern recognition (psychology); Softmax function; Feature extraction; Feature (linguistics); Curse of dimensionality; Classifier (UML); Feature learning; Unsupervised learning; Visualization; Deep 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002347325,0.0001206111,0.0002042548,0.0002697607,0.001028056,0.00009244777,0.0001815092,0.0003042404,0.000007192335],"category_scores_gemma":[0.0002428433,0.00009892081,0.00002025625,0.0001431909,0.0002509067,0.00008068355,0.0007243759,0.000691825,0.000006929072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007021863,"about_ca_system_score_gemma":0.00003718253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007822535,"about_ca_topic_score_gemma":0.0001267304,"domain_scores_codex":[0.9987788,0.00004239128,0.00008880567,0.0004978506,0.0002268386,0.0003653503],"domain_scores_gemma":[0.9989597,0.00004848019,0.00008004555,0.0006377538,0.0001901171,0.00008391505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002577084,0.0003273984,0.01891936,0.003391993,0.0001575383,0.0009749862,0.0006208001,6.790691e-7,0.1287698,0.001747124,0.03453966,0.8079736],"study_design_scores_gemma":[0.005795715,0.01231297,0.00700488,0.0003635397,0.0001085148,0.001613567,0.01246754,0.00799844,0.1757766,0.0006429325,0.7754981,0.0004172115],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839581,0.001119149,0.0003984905,0.008413218,0.0002194799,0.002022853,0.00003388115,0.000183244,0.003651637],"genre_scores_gemma":[0.9941651,0.0001190658,0.001535129,0.00001461648,0.00009669486,0.0001408741,0.00003580353,0.00002060179,0.003872161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8075564,"threshold_uncertainty_score":0.7907077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0330946649350976,"score_gpt":0.3251755122335578,"score_spread":0.2920808472984602,"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."}}