{"id":"W4226516731","doi":"10.48550/arxiv.2112.05758","title":"Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Discriminator; Computer science; Generator (circuit theory); Iterative reconstruction; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Deep learning; Process (computing); Residual; 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":[0.001073289,0.0009846804,0.0008175225,0.000384557,0.0001971927,0.0005249677,0.001198296,0.0008421005,0.001626423],"category_scores_gemma":[0.001878552,0.0004776854,0.0008742998,0.0003227311,0.0006113589,0.0006971013,0.001207291,0.001828154,0.0004442605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006613463,"about_ca_system_score_gemma":0.0005580846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003228448,"about_ca_topic_score_gemma":0.003680668,"domain_scores_codex":[0.9996896,0.0001069193,0.00001267469,0.00007594951,0.00006991907,0.00004499799],"domain_scores_gemma":[0.9994171,0.0003507823,0.00005421674,0.00006131936,0.00008234168,0.00003430879],"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":[0.0001658147,0.00004822659,0.0007846062,0.00005701751,0.00006357889,0.0001239805,0.00003900859,0.9264166,0.004099522,0.003633335,0.001856352,0.06271191],"study_design_scores_gemma":[0.000002668078,0.0000149953,0.0000526299,0.000003270108,0.000004981571,0.0000198469,0.000001907697,0.9979691,0.0005896403,0.001177214,0.0001607297,0.000003027183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03594966,0.0009678581,0.9592375,0.0004091493,0.00007703544,0.0000491026,0.0001435735,0.00109825,0.002067706],"genre_scores_gemma":[0.8265803,0.000773387,0.1622441,0.0006190312,0.00008800678,0.0001341384,0.0008779106,0.0003091688,0.008374022],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003228448,"threshold_uncertainty_score":0.006419301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07356133048886551,"score_gpt":0.2513198144832042,"score_spread":0.1777584839943387,"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."}}