{"id":"W2800464885","doi":"10.1117/1.jbo.23.5.056004","title":"Particle swarm optimization method for small retinal vessels detection on multiresolution fundus images","year":2018,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"False positive paradox; Sensitivity (control systems); Computer science; Particle swarm optimization; Artificial intelligence; Fundus (uterus); Segmentation; Computer vision; Pattern recognition (psychology); CAD; Diabetic retinopathy; Image segmentation; Algorithm; Radiology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007478647,0.0007061916,0.0007735714,0.0007816725,0.0002579026,0.0005371067,0.00048222,0.0007485175,0.0006546499],"category_scores_gemma":[0.001417359,0.0003885682,0.0007416105,0.0005068982,0.0002863169,0.0004441815,0.0003233332,0.0005918164,0.0001776023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004452634,"about_ca_system_score_gemma":0.0006646516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007025235,"about_ca_topic_score_gemma":0.003353683,"domain_scores_codex":[0.9997042,0.00007958562,0.00002121185,0.00007222826,0.00009526351,0.00002756411],"domain_scores_gemma":[0.9996735,0.0001386284,0.0000507082,0.00002012535,0.0001024736,0.00001450952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002089428,0.00007802784,0.002002204,0.0001625539,0.0001350228,0.0001489677,0.0001271146,0.7757466,0.01794219,0.002694563,0.001906977,0.1988467],"study_design_scores_gemma":[0.000006684675,0.00001440263,0.000293548,0.000002608886,0.000005739312,0.000008689167,0.000004295637,0.9986023,0.0006912607,0.0001871983,0.0001802778,0.000002910856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02699376,0.0003410174,0.9712632,0.0001390625,0.00004454716,0.00004218654,0.00002871163,0.0003480961,0.0007994327],"genre_scores_gemma":[0.4660977,0.0005635868,0.5302001,0.0001183529,0.0000715386,0.0001724739,0.0001881221,0.00007239431,0.002515761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007025235,"threshold_uncertainty_score":0.01396871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0328467473817324,"score_gpt":0.347794429698026,"score_spread":0.3149476823162936,"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."}}