{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001611787,0.0001093603,0.0002493642,0.0001986033,0.00006698989,0.000102124,0.000947765,0.00005460801,0.00004554863],"category_scores_gemma":[0.00009633621,0.0001006952,0.0001731563,0.0001555671,0.00008410763,0.0004386728,0.00007534253,0.0001117721,0.00001988367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003286697,"about_ca_system_score_gemma":0.0001103357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005876006,"about_ca_topic_score_gemma":7.029838e-8,"domain_scores_codex":[0.9986842,0.0001634584,0.0005310355,0.0002136149,0.000288617,0.0001190964],"domain_scores_gemma":[0.9964392,0.002009126,0.0005031711,0.0003329264,0.0006348678,0.00008070949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000892153,0.00009592047,0.0001033229,0.000009671647,0.0001191004,0.000005263318,0.0001204102,0.00004137654,0.2181187,0.6623701,0.0001768555,0.11875],"study_design_scores_gemma":[0.00104489,0.00005934191,0.001245019,0.00005600772,0.00005319742,0.0003290873,0.00004601781,0.0003230541,0.02515998,0.9445836,0.02692807,0.000171745],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01557561,0.0004303069,0.9796067,0.0001516153,0.0001808427,0.000327146,0.00007864819,0.00002066988,0.00362844],"genre_scores_gemma":[0.930719,0.0004247135,0.06556248,0.0003708237,0.001088838,0.000134562,0.00003895496,0.00002971909,0.001630887],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9151434,"threshold_uncertainty_score":0.4106232,"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."}}