{"id":"W3096863168","doi":"10.1167/jov.20.11.255","title":"Learning from few examples: Classifying sex from retinal images","year":2020,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Fundus (uterus); Machine learning; Deep learning; Usability; Artificial neural network; Pipeline (software); Pattern recognition (psychology); Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.00028791,0.0001222319,0.0004557938,0.00009183028,0.00008421192,0.00007712817,0.000112924,0.00005471295,0.0004465969],"category_scores_gemma":[0.000559073,0.00008947615,0.0002595898,0.0001738836,0.00003765471,0.0001644101,0.00004424245,0.000709901,0.00004836756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000382773,"about_ca_system_score_gemma":0.00005403706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001122955,"about_ca_topic_score_gemma":3.930774e-7,"domain_scores_codex":[0.9985849,0.0001219608,0.000479576,0.0001776456,0.0004947275,0.000141252],"domain_scores_gemma":[0.9988469,0.0002072006,0.0004215701,0.0001045102,0.0001611496,0.0002587007],"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":[0.0006216339,0.00009749916,0.191714,0.00004391633,0.0002994915,0.000969273,0.002278121,0.0001843241,0.7194155,0.000001246558,0.01194649,0.07242849],"study_design_scores_gemma":[0.007119616,0.004931997,0.8022198,0.005012685,0.002734595,0.0003672844,0.01470093,0.03849377,0.04509819,0.0004867706,0.07816151,0.0006728576],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812128,0.001423359,0.006190127,0.01029833,0.00007815046,0.00002867647,0.000002988711,0.00002737568,0.0007381864],"genre_scores_gemma":[0.9874703,0.0004047897,0.009896373,0.0007316246,0.001232305,1.495247e-7,0.00001557324,0.00001934193,0.0002295411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6743173,"threshold_uncertainty_score":0.4889923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03849775457910067,"score_gpt":0.3198120299745524,"score_spread":0.2813142753954517,"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."}}