{"id":"W4392209496","doi":"10.1109/indicon59947.2023.10440843","title":"GAN-based OCT Image Quality Enhancement: Mapping from Low Quality Cirrus OCT to High Quality EDI OCT","year":2023,"lang":"en","type":"article","venue":"","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Health Authority; University of Waterloo","funders":"","keywords":"Quality (philosophy); Cirrus; Image quality; Computer science; Quality management; Artificial intelligence; Image (mathematics); Geology; Remote sensing; Business; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002128122,0.000500543,0.0007231946,0.0002946871,0.0002381468,0.0001895317,0.0007071889,0.0002271074,0.002199974],"category_scores_gemma":[0.0004012064,0.0005318687,0.000314216,0.002266388,0.0001403781,0.0002658002,0.0001054305,0.0004462457,0.003225638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000227301,"about_ca_system_score_gemma":0.00007369959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004452403,"about_ca_topic_score_gemma":0.0005322756,"domain_scores_codex":[0.9955067,0.0003192767,0.001383992,0.0009102909,0.0008182187,0.001061505],"domain_scores_gemma":[0.996307,0.001271926,0.0001374162,0.001585959,0.0002004313,0.0004972698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000153962,0.0008318565,0.01140141,0.001309207,0.0005797014,0.00001907872,0.001602087,0.01198573,0.9048283,0.0297283,0.01834356,0.01921688],"study_design_scores_gemma":[0.002146743,0.0001218253,0.6753142,0.000219171,0.00008515321,3.405409e-7,0.001374572,0.01525955,0.2828128,0.01284126,0.006919915,0.002904473],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7215425,0.00004035463,0.2543921,0.001192427,0.0004255443,0.0009531525,0.0006294109,0.002898267,0.01792624],"genre_scores_gemma":[0.9836742,0.00001537852,0.01425275,0.0005571254,0.000216713,0.0004898778,0.0004112786,0.00007242357,0.0003102695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6639128,"threshold_uncertainty_score":0.9997133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04350325497004752,"score_gpt":0.3250911188236146,"score_spread":0.281587863853567,"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."}}