{"id":"W4408611798","doi":"10.1109/csis-iac63491.2024.10919256","title":"A Denoising UNet Model with ConvNeXt Block for MRI Reconstruction","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Noise reduction; Block (permutation group theory); Computer science; Artificial intelligence; Mathematics","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":[0.0004794498,0.0007031803,0.0006846645,0.0003434069,0.0002137524,0.0006053665,0.001484624,0.0009247534,0.002752801],"category_scores_gemma":[0.0008340366,0.0003726967,0.0007319019,0.0003956529,0.0003491361,0.0007924006,0.0007403895,0.001127988,0.001106282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006580634,"about_ca_system_score_gemma":0.001096666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008987798,"about_ca_topic_score_gemma":0.01076618,"domain_scores_codex":[0.9998802,0.00001770159,0.000006931579,0.00003348412,0.00004130662,0.00002036978],"domain_scores_gemma":[0.9998567,0.00003880506,0.00001641176,0.00002097653,0.00005118591,0.00001591116],"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":[0.0002980083,0.00009768766,0.001196672,0.0001279861,0.0001122923,0.0001833692,0.00005280137,0.7503043,0.01855403,0.01189348,0.006047951,0.2111313],"study_design_scores_gemma":[0.000004494574,0.00002114347,0.00004528346,0.000004706394,0.000007983865,0.00002720263,0.000002055205,0.9964166,0.00193742,0.0007449542,0.0007844166,0.000003762872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01647863,0.0006702866,0.9781438,0.0003171668,0.00009836965,0.00005048962,0.000253155,0.001187335,0.002800848],"genre_scores_gemma":[0.5449523,0.001161703,0.4306634,0.0005733968,0.0001181883,0.0003011374,0.001848959,0.0003812546,0.01999964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008987798,"threshold_uncertainty_score":0.01787096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02829465169859279,"score_gpt":0.3243505397154797,"score_spread":0.2960558880168869,"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."}}