{"id":"W2912229373","doi":"10.1002/cta.2591","title":"Real‐time removal of impulse noise from MR images for radiosurgery applications","year":2019,"lang":"en","type":"article","venue":"International Journal of Circuit Theory and Applications","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Field-programmable gate array; Computer science; Noise reduction; Noise (video); Medical imaging; Computer vision; Artificial intelligence; Impulse noise; Image noise; Image processing; Computer hardware; Image (mathematics); Pixel","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":[0.0001919348,0.0002860048,0.0002278735,0.0004094498,0.00009875857,0.0002388077,0.0002380469,0.000321348,0.001142754],"category_scores_gemma":[0.0005450961,0.0001152959,0.0002172194,0.0001882646,0.0001437485,0.0002434677,0.0001557376,0.0002850341,0.000457674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001272875,"about_ca_system_score_gemma":0.0001670473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003673596,"about_ca_topic_score_gemma":0.0006209494,"domain_scores_codex":[0.9999148,0.0000116725,0.000005113962,0.0000117354,0.00004910071,0.000007588975],"domain_scores_gemma":[0.9998149,0.00006549726,0.00002690975,0.00002039279,0.00006174696,0.0000105669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004388654,0.0000393355,0.001161704,0.0002137634,0.00003663682,0.0003482583,0.00009177194,0.02545837,0.6393166,0.00145515,0.002152595,0.3292869],"study_design_scores_gemma":[0.00002956955,0.0003477616,0.004412508,0.00003645304,0.00007433741,0.001242216,0.0000619424,0.4986392,0.4822392,0.001560142,0.01131789,0.00003880103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.112338,0.001021645,0.8826302,0.0002072627,0.0000941335,0.0000348396,0.00006560638,0.001560146,0.002048125],"genre_scores_gemma":[0.5528761,0.0008727694,0.440949,0.0001300377,0.00006995121,0.00004580649,0.0002144941,0.0001837522,0.004658103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001142754,"threshold_uncertainty_score":0.003822863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01120298142478063,"score_gpt":0.28182837590649,"score_spread":0.2706253944817094,"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."}}