{"id":"W4311257085","doi":"10.1016/j.bspc.2022.104450","title":"Multi-level GAN based enhanced CT scans for liver cancer diagnosis","year":2022,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Preprocessor; Artificial intelligence; Pattern recognition (psychology); Metric (unit); Generative adversarial network; Similarity (geometry); Computer-aided diagnosis; Noise (video); Image (mathematics); Computer vision","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":[0.0004356457,0.0002168672,0.000285515,0.0001428897,0.0008280479,0.0001927969,0.0006994825,0.0000422796,0.00006783359],"category_scores_gemma":[0.0000907935,0.0001947673,0.00006617122,0.0004746684,0.0002221541,0.0003565356,0.0001113952,0.0002390362,8.08737e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001095266,"about_ca_system_score_gemma":0.0004559408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004394667,"about_ca_topic_score_gemma":0.000004233364,"domain_scores_codex":[0.9980595,0.0000817457,0.0002905975,0.0006265538,0.0004819025,0.0004597601],"domain_scores_gemma":[0.9990276,0.0002422677,0.0001845586,0.0001772575,0.0001532738,0.0002151114],"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":[0.0000590128,0.0002641364,0.0001054769,0.0001387767,0.00001582274,0.00001819364,0.0001595481,0.00004113652,0.02649034,0.00003805565,0.000775012,0.9718945],"study_design_scores_gemma":[0.00280467,0.0003161835,0.0001768541,0.0001262074,0.00003534222,0.0000121378,0.00002973232,0.9693295,0.01213336,0.0008484492,0.01381294,0.0003746021],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003061085,0.00363459,0.9927209,0.002330439,0.0000904675,0.0003797478,0.0001204301,0.0004032184,0.00001410942],"genre_scores_gemma":[0.7965512,0.00002247153,0.1979898,0.00227843,0.00008581125,0.002973816,0.000007797142,0.00001967616,0.00007095761],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9715199,"threshold_uncertainty_score":0.7942379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0309242260044258,"score_gpt":0.2954686991091195,"score_spread":0.2645444731046938,"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."}}