{"id":"W2053944582","doi":"10.1109/tip.2013.2240005","title":"3-D Curvilinear Structure Detection Filter Via Structure-Ball Analysis","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Curvilinear coordinates; Artificial intelligence; Computer vision; Computer science; Voxel; Segmentation; Pattern recognition (psychology); Ball (mathematics); Computation; Multiresolution analysis; Visualization; Filter (signal processing); Image processing; Mathematics; Wavelet; Wavelet transform; Algorithm; Image (mathematics); Geometry; Discrete wavelet transform","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003996597,0.0002604416,0.0003358876,0.0004486034,0.0003105339,0.0001237287,0.00009073027,0.00016956,0.00205614],"category_scores_gemma":[0.000006639097,0.0002067234,0.0002466326,0.0009935073,0.0001119454,0.0003676424,0.000001000719,0.0005082527,0.00006678555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006509417,"about_ca_system_score_gemma":0.00005024156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001842761,"about_ca_topic_score_gemma":0.0002001303,"domain_scores_codex":[0.9986326,0.00003908965,0.0002995611,0.0003950572,0.0003165431,0.0003171415],"domain_scores_gemma":[0.9991909,0.00002205561,0.00009705222,0.0002690158,0.0002616751,0.0001592799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001715361,0.0001743829,0.0007058787,0.0003032974,0.0004781385,0.00001244399,0.000443122,0.0008127347,0.7319112,3.103896e-7,0.00004778131,0.2649392],"study_design_scores_gemma":[0.002139911,0.0004679233,0.03839764,0.0001624744,0.003930184,0.0002326941,0.0005560778,0.1849868,0.7675097,0.0005223333,0.0003350081,0.0007592675],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4477904,0.00007130915,0.5511419,0.000288198,0.0001376785,0.0002545512,0.00001549531,0.0001115038,0.0001889894],"genre_scores_gemma":[0.9924927,0.000009529502,0.006550092,0.0003667974,0.00007632723,0.00002199308,0.00001728156,0.00003893717,0.000426313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5447024,"threshold_uncertainty_score":0.9988561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00603068363889255,"score_gpt":0.2411335928065276,"score_spread":0.235102909167635,"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."}}