{"id":"W2780788572","doi":"10.3390/jimaging5020026","title":"PixelBNN: Augmenting the PixelCNN with Batch Normalization and the Presentation of a Fast Architecture for Retinal Vessel Segmentation","year":2019,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Segmentation; Normalization (sociology); Fundus (uterus); Image segmentation; Retinal; Hue; Image quality; Image processing","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":[0.0009367875,0.001176327,0.0006731754,0.0006997875,0.0003777798,0.00085351,0.001845085,0.001095679,0.005381588],"category_scores_gemma":[0.001997768,0.0005650271,0.0007744557,0.0007514367,0.0004691609,0.001227492,0.001186804,0.001587484,0.002753389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011484,"about_ca_system_score_gemma":0.001143667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01385482,"about_ca_topic_score_gemma":0.02106184,"domain_scores_codex":[0.9997019,0.00004001109,0.00001679305,0.0001120254,0.00008482975,0.00004438646],"domain_scores_gemma":[0.9996196,0.00009452336,0.00003091173,0.00008506539,0.0001377057,0.00003215915],"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":[0.0005295934,0.0002052445,0.001855619,0.0002914595,0.0002589035,0.0001890668,0.0001108174,0.1297283,0.04337666,0.005546867,0.0280508,0.7898567],"study_design_scores_gemma":[0.00002890205,0.0001177329,0.0008138084,0.00004270949,0.00005303706,0.0001183065,0.00002073965,0.9667805,0.01812187,0.004445328,0.009430219,0.00002669225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04172567,0.001943493,0.9286123,0.0005757781,0.0004866025,0.0001985883,0.001392892,0.01937186,0.005692854],"genre_scores_gemma":[0.2985878,0.001346302,0.6739886,0.001017655,0.0002065028,0.0003883836,0.005967353,0.001420114,0.01707725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01385482,"threshold_uncertainty_score":0.02754837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004571420200184811,"score_gpt":0.2578317520577964,"score_spread":0.2532603318576116,"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."}}