{"id":"W2888443510","doi":"10.1016/j.media.2018.08.005","title":"Spine-GAN: Semantic segmentation of multiple spinal structures","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":215,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Concatenation (mathematics); Computer vision; Mathematics","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":[0.000328921,0.0001621659,0.0004628031,0.0003962535,0.00005688596,0.00002851942,0.0002473404,0.00009116081,0.003258341],"category_scores_gemma":[0.0005087292,0.0001327817,0.0002930595,0.001616835,0.0003340897,0.0001046053,0.00003033013,0.0001826339,0.00006453576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000239781,"about_ca_system_score_gemma":0.00002185099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001672494,"about_ca_topic_score_gemma":0.00009518809,"domain_scores_codex":[0.9981835,0.00005014722,0.0004683992,0.0002268142,0.000806484,0.0002646644],"domain_scores_gemma":[0.9991916,0.00004370778,0.00006617187,0.0003013426,0.0001264577,0.0002707005],"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":[0.00004812781,0.0005075666,0.1954446,0.001372695,0.02272337,0.0004415877,0.002062297,0.005463111,0.2865757,0.0002170637,0.02118663,0.4639573],"study_design_scores_gemma":[0.0005026392,0.00004990913,0.02051612,0.0000558246,0.002486893,0.00000555293,0.0001592042,0.9219974,0.05333039,0.0001496981,0.0004928932,0.0002534305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4736246,0.0003186875,0.5240651,0.0003466078,0.0001528701,0.00005241117,0.000007286718,0.0001708102,0.001261631],"genre_scores_gemma":[0.994487,0.00007160411,0.00482116,0.0001509127,0.0003045198,0.000004261472,0.00005192708,0.00001665048,0.00009197662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9165343,"threshold_uncertainty_score":0.9976528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007484894842775308,"score_gpt":0.27293141559568,"score_spread":0.2654465207529047,"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."}}