{"id":"W4416338272","doi":"10.1016/j.eij.2025.100823","title":"Advanced hybrid UNet architectures for retinal vessel segmentation","year":2025,"lang":"en","type":"article","venue":"Egyptian Informatics Journal","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Convolutional neural network; Fundus (uterus); Deep learning; Transfer of learning; Automation; Image segmentation; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007110839,0.0009574249,0.0005080824,0.001017546,0.0003313348,0.001089937,0.001430629,0.001299193,0.002345244],"category_scores_gemma":[0.001701901,0.0003520148,0.0009590471,0.0006699861,0.0003541722,0.001315546,0.0008384627,0.0009347684,0.001035743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001126046,"about_ca_system_score_gemma":0.0009378479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008266952,"about_ca_topic_score_gemma":0.01205886,"domain_scores_codex":[0.9997464,0.00002952658,0.00001604273,0.00009085984,0.00006817078,0.00004891374],"domain_scores_gemma":[0.9996004,0.00008713397,0.00004615178,0.00008203187,0.0001540565,0.00003032237],"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":[0.000433184,0.0002713789,0.005241649,0.0001935875,0.0002590714,0.0003890101,0.0001023111,0.3748545,0.03307771,0.007789181,0.01506081,0.5623276],"study_design_scores_gemma":[0.00001023511,0.00006629802,0.0007173343,0.0000225709,0.0000308395,0.0001158446,0.00001114693,0.9870166,0.007721634,0.002096677,0.002178512,0.00001230062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1558958,0.00373301,0.8135153,0.001066972,0.0003319368,0.0001814485,0.001445567,0.01137705,0.01245289],"genre_scores_gemma":[0.7208767,0.001402757,0.2575835,0.0009495478,0.0001214454,0.0001866694,0.003804978,0.0004303543,0.014644],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008266952,"threshold_uncertainty_score":0.01643765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007165265375609338,"score_gpt":0.3034313947068736,"score_spread":0.2962661293312643,"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."}}