{"id":"W2793498597","doi":"10.1117/12.2292479","title":"Comparison of Gaussian filter versus wavelet-based denoising on graph-based segmentation of retinal OCT images","year":2018,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Noise reduction; Wavelet; Pattern recognition (psychology); Computer vision; Image denoising; Segmentation; Wavelet transform; Gaussian; Graph; Filter (signal processing); Physics; Theoretical computer science","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":[0.002590202,0.000574133,0.0005773946,0.001407673,0.0001893098,0.000975429,0.0005421201,0.0009448504,0.000693094],"category_scores_gemma":[0.008291178,0.0002532539,0.0007516741,0.000647157,0.0003767194,0.000887128,0.0003908054,0.0003952331,0.0002831117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004826721,"about_ca_system_score_gemma":0.0007010153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004081225,"about_ca_topic_score_gemma":0.005122676,"domain_scores_codex":[0.9992029,0.0002784051,0.00007899717,0.0001538599,0.000209583,0.00007621379],"domain_scores_gemma":[0.996958,0.001939956,0.000218719,0.0002018983,0.0005876352,0.00009389117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.009536871,0.0007002401,0.01097712,0.001170854,0.0006108824,0.0002720944,0.0007016229,0.216904,0.3013909,0.0021491,0.001177666,0.4544087],"study_design_scores_gemma":[0.00009000015,0.00118599,0.02048178,0.00006260438,0.0002594024,0.000277138,0.0001815103,0.9007745,0.07473131,0.0008014729,0.001091996,0.00006231638],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7707624,0.001393377,0.2242005,0.0002355543,0.0001331862,0.0001471141,0.0002089976,0.0009030722,0.002015751],"genre_scores_gemma":[0.7320919,0.001026421,0.2643916,0.0001288125,0.00003574381,0.00007638337,0.0006823331,0.0003692646,0.001197439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004081225,"threshold_uncertainty_score":0.01369852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04616463342124388,"score_gpt":0.378560831617731,"score_spread":0.3323961981964871,"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."}}