{"id":"W3013935260","doi":"10.1117/12.2567580","title":"The Foveal Avascular Zone Image Database (FAZID)","year":2020,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Foveal; Artificial intelligence; Computer science; Foveal avascular zone; Ground truth; Segmentation; Computer vision; Fundus (uterus); Image segmentation; Optical coherence tomography; Retinal; Medicine; Ophthalmology","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":[0.0007095159,0.001274067,0.001019711,0.004884775,0.0005325429,0.001066992,0.00172672,0.001413907,0.008075639],"category_scores_gemma":[0.001360131,0.0003795683,0.0007868381,0.002437036,0.0002720244,0.00072157,0.001123215,0.0008606112,0.007855346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008473187,"about_ca_system_score_gemma":0.001011634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01056341,"about_ca_topic_score_gemma":0.02286677,"domain_scores_codex":[0.9994677,0.0000407286,0.00005845397,0.0001337033,0.0002217177,0.00007773319],"domain_scores_gemma":[0.999374,0.00007153579,0.00008099237,0.0001618962,0.0002368532,0.00007473195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001320949,0.0006906293,0.00913153,0.003391395,0.0002960604,0.0008978336,0.0001512976,0.002184867,0.02232117,0.001733398,0.8020061,0.1558748],"study_design_scores_gemma":[0.0007895289,0.0005840784,0.1439539,0.001008043,0.0003214948,0.006202213,0.0005949767,0.02300661,0.03880447,0.002449454,0.7820715,0.0002137686],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07194173,0.006868829,0.01351648,0.0005712307,0.0003337506,0.001396919,0.8711805,0.01703453,0.01715614],"genre_scores_gemma":[0.03270778,0.001181331,0.02467109,0.0001577429,0.00006025435,0.0004811381,0.9364634,0.0005175872,0.003759707],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01056341,"threshold_uncertainty_score":0.02701563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712607594932724,"score_gpt":0.275115862169509,"score_spread":0.2579897862201817,"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."}}