{"id":"W4407396097","doi":"10.1007/s10278-025-01434-5","title":"3D Wasserstein Generative Adversarial Network with Dense U-Net-Based Discriminator for Preclinical fMRI Denoising","year":2025,"lang":"en","type":"article","venue":"Journal of Imaging Informatics in Medicine","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Alliance de recherche numérique du Canada; Mitacs","keywords":"Discriminator; Computer science; Noise reduction; Artificial intelligence; Noise (video); Pattern recognition (psychology); Pipeline (software); Preprocessor; Communication noise; Functional magnetic resonance imaging; Feature (linguistics); Computer vision; Image (mathematics); Detector","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":[0.001601023,0.0001686755,0.0004259275,0.0002326015,0.00006754555,0.00003183496,0.0002548822,0.00007944361,0.00000486741],"category_scores_gemma":[0.0008967875,0.0001193758,0.0001095249,0.0002240615,0.0002002543,0.00002653065,0.00005506135,0.0002606501,1.914454e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006792503,"about_ca_system_score_gemma":0.000283591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000766351,"about_ca_topic_score_gemma":0.00003001526,"domain_scores_codex":[0.9983003,0.00006941025,0.001054839,0.0001055301,0.0002372051,0.0002327255],"domain_scores_gemma":[0.9984624,0.0001661632,0.0006193115,0.0002533115,0.0004306119,0.00006819593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01307275,0.001082788,0.1141636,0.002158872,0.003018962,0.0003054097,0.006304291,0.09859184,0.149915,0.000800707,0.5131643,0.09742158],"study_design_scores_gemma":[0.04346491,0.007777265,0.003120077,0.009972718,0.004051848,0.0003902113,0.01162933,0.3335236,0.4304232,0.00318229,0.1505555,0.001909022],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09623198,0.0009295049,0.8993792,0.001553652,0.0002061823,0.0003298337,0.000001413086,0.000009896204,0.001358363],"genre_scores_gemma":[0.7299034,0.0001705133,0.2652234,0.003608392,0.0008511512,0.00001475686,0.00004943501,0.00002360296,0.0001552946],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6341558,"threshold_uncertainty_score":0.4868002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053979663456769,"score_gpt":0.3231101680841182,"score_spread":0.3125703714495505,"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."}}