{"id":"W2051453269","doi":"10.1117/12.2043325","title":"Retinal image quality assessment using generic features","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Image quality; Robustness (evolution); Pixel; Support vector machine; Pattern recognition (psychology); Segmentation; Image segmentation; Classifier (UML); Computer vision; Image (mathematics)","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"],"consensus_categories":[],"category_scores_codex":[0.001293752,0.0003290227,0.0006471177,0.0001291119,0.0001113495,0.0001304527,0.0005341391,0.0001565486,0.00001852199],"category_scores_gemma":[0.0008894835,0.0002567408,0.0009982514,0.0003909543,0.0002879641,0.0003272136,0.0001427395,0.0004448442,0.000001247895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002108208,"about_ca_system_score_gemma":0.00005511092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003627783,"about_ca_topic_score_gemma":8.511871e-8,"domain_scores_codex":[0.9974051,3.736424e-8,0.0007667884,0.0004478788,0.0009696651,0.0004106044],"domain_scores_gemma":[0.9970478,0.0001366831,0.0004898189,0.00008844001,0.002056342,0.0001809309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001076302,0.0001505183,0.0033232,0.0007594832,0.0005297315,1.722172e-7,0.000085827,0.00004602696,0.8604078,0.1320055,0.00216601,0.0004180701],"study_design_scores_gemma":[0.007119468,0.00189118,0.09770274,0.00266021,0.003639959,0.0003692994,0.00511467,0.3348098,0.5279528,0.004497116,0.01225877,0.001983973],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895294,0.00008162207,0.0003226175,0.003882641,0.0001362808,0.000312452,0.00001724048,0.00008366416,0.005634131],"genre_scores_gemma":[0.6131034,0.00006188018,0.3852384,0.0002626067,0.0007482074,0.00004402158,0.00001429958,0.00006418306,0.0004629785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3849157,"threshold_uncertainty_score":0.9999885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02039658983193441,"score_gpt":0.2989988568229384,"score_spread":0.2786022669910039,"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."}}