{"id":"W4285057981","doi":"10.1109/tim.2022.3189735","title":"A Dual-Discriminator Fourier Acquisitive GAN for Generating Retinal Optical Coherence Tomography Images","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Isfahan University of Medical Sciences","keywords":"Discriminator; Artificial intelligence; Optical coherence tomography; Computer science; Pattern recognition (psychology); Discriminative model; Fourier transform; Similarity (geometry); Similarity measure; Computer vision; Mathematics; Image (mathematics); Optics; Physics","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":[0.0002582577,0.0004423462,0.0002061823,0.0002031975,0.00008906097,0.0001954718,0.0004689403,0.0003357857,0.0009182828],"category_scores_gemma":[0.0004157668,0.0001327755,0.000320831,0.00017483,0.0002253774,0.0002958186,0.0003631205,0.0006632376,0.0001923018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003139668,"about_ca_system_score_gemma":0.0002772638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001545396,"about_ca_topic_score_gemma":0.001944999,"domain_scores_codex":[0.999898,0.00002452798,0.000003145803,0.00002675144,0.00003328588,0.00001439609],"domain_scores_gemma":[0.9999009,0.0000382876,0.000009758186,0.00001691725,0.00002408809,0.00001001711],"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":[0.0002370405,0.0001412192,0.001837488,0.0001010456,0.00006254193,0.000365711,0.00005650295,0.7341871,0.07343681,0.009006434,0.005254589,0.1753135],"study_design_scores_gemma":[0.000005343314,0.00003345992,0.000190641,0.000003086792,0.00000369779,0.00006594643,0.000002955263,0.9929783,0.005574582,0.0005714366,0.0005658335,0.00000469078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1016964,0.0005376397,0.8892449,0.0004886086,0.0001200815,0.00009355233,0.0002473568,0.001299728,0.006271902],"genre_scores_gemma":[0.8089272,0.0003613575,0.1848896,0.0003089003,0.00003699288,0.0001064577,0.0004824542,0.00007976757,0.004807096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001545396,"threshold_uncertainty_score":0.003072739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03647228675978978,"score_gpt":0.2901220701480898,"score_spread":0.2536497833883,"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."}}