{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009016219,0.0008745364,0.0006413052,0.0003781439,0.0001576496,0.0005088336,0.001108383,0.0008221477,0.00203656],"category_scores_gemma":[0.00185031,0.0004168019,0.0007268753,0.0003393354,0.0005407489,0.0007822963,0.001206743,0.001617882,0.0005571603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004925851,"about_ca_system_score_gemma":0.0004509696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001472805,"about_ca_topic_score_gemma":0.002466859,"domain_scores_codex":[0.9997354,0.00008570048,0.00001029076,0.00006516908,0.00007099001,0.0000324673],"domain_scores_gemma":[0.9995102,0.0002716872,0.00005346222,0.00006587024,0.00006476046,0.0000340097],"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":[0.0002381616,0.00006530521,0.001078661,0.0001116498,0.0001098462,0.0002095824,0.00006201457,0.8409887,0.01145244,0.008600589,0.003536857,0.1335462],"study_design_scores_gemma":[0.00000390621,0.00002414239,0.00007200626,0.000005034958,0.000007706752,0.00005130927,0.000002862967,0.9955856,0.001560366,0.002238487,0.0004441977,0.000004346535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01259811,0.0004902345,0.9844643,0.0002148072,0.00004742188,0.00003469902,0.0000872469,0.0005764526,0.001486797],"genre_scores_gemma":[0.6842801,0.0007827787,0.303428,0.0007011695,0.0001045825,0.0001500181,0.0007542516,0.0003453951,0.009453782],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00203656,"threshold_uncertainty_score":0.00681299,"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."}}