{"id":"W4323240587","doi":"10.5220/0011754700003417","title":"EFL-Net: An Efficient Lightweight Neural Network Architecture for Retinal Vessel Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Artificial neural network; Architecture; Segmentation; Retinal; Artificial intelligence; Computer vision; Computer architecture; 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":[0.0002539827,0.0001293403,0.0002068747,0.0001222869,0.0001407252,0.00003956232,0.00006850282,0.0000410358,0.00008001685],"category_scores_gemma":[0.00003434997,0.00009250021,0.0001361104,0.0005090355,0.00003484211,0.00002679126,0.00001906218,0.0001187826,0.00004788748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002085332,"about_ca_system_score_gemma":0.0000207317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001486895,"about_ca_topic_score_gemma":0.000003846833,"domain_scores_codex":[0.9989256,0.00004010041,0.000198121,0.000289511,0.0002265276,0.0003200956],"domain_scores_gemma":[0.9994466,0.00007674336,0.00005282561,0.0002098783,0.00007458548,0.0001393218],"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":[0.002512328,0.0007876369,0.08492164,0.0007669001,0.0007144774,0.0003530995,0.00388291,0.3669408,0.09993158,0.001676307,0.2171505,0.2203618],"study_design_scores_gemma":[0.002226423,0.001070412,0.03700379,0.0001510929,0.0005672957,0.00010711,0.0007480033,0.9288335,0.01082874,0.0005709169,0.01747967,0.000412978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667121,0.00009490737,0.02286777,0.007406569,0.0002102309,0.0004109535,0.00000600041,0.0003739686,0.001917462],"genre_scores_gemma":[0.9796265,0.00001365506,0.01111802,0.001078302,0.0009987616,0.00004587682,0.0003988609,0.00003413187,0.006685936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5618928,"threshold_uncertainty_score":0.3772049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660418521028525,"score_gpt":0.3037652407382569,"score_spread":0.2871610555279717,"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."}}