{"id":"W4385388935","doi":"10.18280/ria.370307","title":"Automated Retinal Hard Exudate Detection Using Novel Rhombus Multilevel Segmentation Algorithm","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Rhombus; Segmentation; Exudate; Computer science; Algorithm; Artificial intelligence; Pattern recognition (psychology); Retinal; Mathematics; Ophthalmology; Geometry; Biology; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002712278,0.0003982283,0.0006652073,0.001556375,0.0003623402,0.0007007408,0.0007125648,0.0006578812,0.001445072],"category_scores_gemma":[0.0007129445,0.0002932073,0.0007140376,0.0007262636,0.0002114648,0.0004889703,0.0004311915,0.0003847941,0.0004996132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004166146,"about_ca_system_score_gemma":0.0006260077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003438446,"about_ca_topic_score_gemma":0.004518563,"domain_scores_codex":[0.99964,0.00003866339,0.00002717267,0.00009982137,0.0001576724,0.00003672584],"domain_scores_gemma":[0.9996924,0.00006626089,0.00005143673,0.00004399042,0.0001270272,0.00001883277],"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":[0.0003665451,0.0001395542,0.004801446,0.0002391003,0.000148708,0.0003003449,0.000171039,0.02017067,0.2589383,0.001739876,0.002851182,0.7101332],"study_design_scores_gemma":[0.00004287192,0.00018757,0.01198909,0.0000392447,0.0001107074,0.0009671723,0.00006432457,0.8782743,0.1021403,0.001214145,0.004902948,0.00006740585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1327079,0.0006131791,0.8603267,0.0002558869,0.00005725591,0.0001494001,0.000250801,0.003225097,0.002413773],"genre_scores_gemma":[0.3656713,0.0004263749,0.6310865,0.00008589029,0.00003881341,0.0001204613,0.0003196999,0.0001211761,0.002129759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003438446,"threshold_uncertainty_score":0.006836832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08231580467314423,"score_gpt":0.3465055655659365,"score_spread":0.2641897608927922,"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."}}