{"id":"W4402977647","doi":"10.2139/ssrn.4971679","title":"A Comprehensive Study of Conditional Generative Adversarial Networks for Noise Reduction in Optical Coherence Tomography","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Optical coherence tomography; Reduction (mathematics); Generative grammar; Coherence (philosophical gambling strategy); Adversarial system; Computer science; Noise reduction; Artificial intelligence; Noise (video); Generative adversarial network; Physics; Mathematics; Optics; Image (mathematics); Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003156934,0.001556766,0.00123017,0.0008785483,0.0005472309,0.001434892,0.001757088,0.002073395,0.002055535],"category_scores_gemma":[0.01057805,0.0009527367,0.000935466,0.0009181275,0.002467091,0.002148111,0.003068385,0.00289109,0.0003487116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001068842,"about_ca_system_score_gemma":0.001062521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002659897,"about_ca_topic_score_gemma":0.002690589,"domain_scores_codex":[0.9988511,0.0005517649,0.00003518815,0.0001628442,0.0003145526,0.00008443557],"domain_scores_gemma":[0.9932781,0.005616277,0.0002766008,0.0003111929,0.0003749475,0.0001427976],"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":[0.00003480183,0.00003011141,0.0003585414,0.0001331393,0.000063488,0.00007831203,0.00007463634,0.8225336,0.001758492,0.1569955,0.001429588,0.0165098],"study_design_scores_gemma":[0.000001962912,0.00001139877,0.00007524141,0.00001585611,0.000008414941,0.00002498172,0.00000533527,0.9588621,0.0003638014,0.04004751,0.000577678,0.000005765458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00749575,0.001562339,0.9860249,0.000632455,0.0000527786,0.00002669934,0.00006437932,0.00009433907,0.004046313],"genre_scores_gemma":[0.7190494,0.006922862,0.2469202,0.001087051,0.0007484238,0.0003011103,0.0005649879,0.00049832,0.02390773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003156934,"threshold_uncertainty_score":0.01669562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01408117843182049,"score_gpt":0.2651085847923774,"score_spread":0.2510274063605569,"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."}}