{"id":"W4200631312","doi":"10.1007/s10489-021-03092-w","title":"Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; British Heart Foundation","keywords":"Discriminator; Computer science; Generator (circuit theory); Artificial intelligence; Enhanced Data Rates for GSM Evolution; Iterative reconstruction; Deep learning; Residual; Process (computing); Computer vision; Algorithm; Detector; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001528565,0.0001956104,0.0002618149,0.0000582264,0.0005257789,0.00002594648,0.0001302358,0.0000326048,0.00007369011],"category_scores_gemma":[0.00001158913,0.0001744971,0.00006182164,0.0003133139,0.00009731772,0.0001974241,0.0001168674,0.000235326,0.00000700531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001784088,"about_ca_system_score_gemma":0.0001116082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001880875,"about_ca_topic_score_gemma":0.000004555453,"domain_scores_codex":[0.9988131,0.00001320358,0.0003522891,0.0002830219,0.000211905,0.0003264628],"domain_scores_gemma":[0.9992572,0.0000505834,0.0002051313,0.0002780631,0.0001216978,0.00008729717],"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":[0.001772814,0.0003553512,0.0001035885,0.0001746439,0.00008115345,0.000006489833,0.004554024,0.5774621,0.01069418,0.1769059,0.002898596,0.2249911],"study_design_scores_gemma":[0.003392271,0.000897615,0.0001823902,0.0002693868,0.0005112634,0.0001605921,0.0144597,0.7202108,0.1049318,0.01252335,0.1409421,0.001518839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00124658,0.00009159974,0.9951261,0.0002473507,0.0000832127,0.002109834,0.00004583524,0.0001287463,0.0009207308],"genre_scores_gemma":[0.3304758,0.00006530262,0.6659773,0.0009350395,0.0001977679,0.001978713,0.0002305799,0.00003001359,0.0001094773],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3292292,"threshold_uncertainty_score":0.7115784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03037095963155466,"score_gpt":0.3173209932615673,"score_spread":0.2869500336300126,"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."}}