{"id":"W4386352896","doi":"10.1109/isbi53787.2023.10230810","title":"Uno-Qa: an Unsupervised Anomaly-Aware Framework with Test-Time Clustering for Octa Image Quality Assessment","year":2023,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Cluster analysis; Artificial intelligence; Image quality; Quality (philosophy); Pattern recognition (psychology); Representation (politics); Feature (linguistics); Embedding; Set (abstract data type); Feature extraction; Test set; Dimension (graph theory); Image (mathematics); Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001116865,0.001196222,0.001320327,0.002007517,0.000451574,0.001004143,0.002340761,0.001142001,0.001157087],"category_scores_gemma":[0.003266445,0.0003924005,0.0008304359,0.001087714,0.0005768308,0.001134562,0.001451418,0.001188149,0.0008501217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007591806,"about_ca_system_score_gemma":0.001092991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009458208,"about_ca_topic_score_gemma":0.016694,"domain_scores_codex":[0.99913,0.0001315983,0.00004423104,0.0003127569,0.0002661182,0.0001154297],"domain_scores_gemma":[0.9987532,0.0001982384,0.0001632933,0.0002587496,0.000515337,0.0001111216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005359852,0.0005400193,0.01156423,0.0001927004,0.000250642,0.0002003835,0.0002375832,0.1768608,0.03908438,0.003298657,0.01343667,0.7537981],"study_design_scores_gemma":[0.00001291516,0.00006358886,0.001535763,0.000007975992,0.00001990693,0.00007823756,0.00002811968,0.9896761,0.00518516,0.002196193,0.00117739,0.00001874016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02636323,0.00040809,0.9658118,0.0001340507,0.00005020906,0.0001474199,0.0004065916,0.005777935,0.0009006271],"genre_scores_gemma":[0.4195743,0.00038937,0.5710105,0.0003178738,0.0001229866,0.0003160505,0.003233583,0.0007757121,0.00425965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009458208,"threshold_uncertainty_score":0.01880634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04395622963657569,"score_gpt":0.3935948206966587,"score_spread":0.349638591060083,"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."}}