{"id":"W4205996016","doi":"10.21203/rs.3.rs-1219874/v1","title":"EnGAN: Enhancement Generative Adversarial Network in Medical Image Segmentation","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Adversarial system; Generative grammar; Image (mathematics); Artificial intelligence; Segmentation; Generative adversarial network; Computer science; Image segmentation; Computer vision","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":[0.001313681,0.0007445726,0.0006790882,0.0005820362,0.0002398144,0.0006644476,0.001114137,0.00133594,0.003955977],"category_scores_gemma":[0.002595666,0.0006658885,0.0006270004,0.0004492334,0.0006294079,0.0008635988,0.001662573,0.001772972,0.001281599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005379359,"about_ca_system_score_gemma":0.0005469762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001887572,"about_ca_topic_score_gemma":0.002489247,"domain_scores_codex":[0.9996043,0.0001488583,0.00001247948,0.00008865786,0.0001206463,0.00002511312],"domain_scores_gemma":[0.9994072,0.000376764,0.00003096514,0.00009117782,0.00006815181,0.00002579066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002355555,0.0000733053,0.0005679473,0.0001312865,0.0001053444,0.0001503697,0.000077135,0.6627246,0.01926584,0.02071563,0.009987887,0.2859651],"study_design_scores_gemma":[0.000006869701,0.00001217512,0.00007409922,0.000005291429,0.000005628638,0.00004306206,0.000002329252,0.9897019,0.004152982,0.004397737,0.001593664,0.000004259892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003080707,0.0002000499,0.9942983,0.0002026531,0.0000463934,0.00003585914,0.00008501916,0.001141862,0.000909159],"genre_scores_gemma":[0.2227243,0.0006174432,0.7597802,0.0006020087,0.0001697349,0.0002258984,0.0007037584,0.001175542,0.01400116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003955977,"threshold_uncertainty_score":0.01323408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04512820479458916,"score_gpt":0.3795989158309855,"score_spread":0.3344707110363963,"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."}}