{"id":"W2808043578","doi":"10.1186/s12938-018-0514-4","title":"Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters","year":2018,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; NeuroRx Research (Canada)","funders":"National Institute on Aging; Norges Forskningsråd","keywords":"Gaussian blur; Artificial intelligence; Gaussian filter; Computer science; Computer vision; Noise (video); Filter (signal processing); Image quality; Gaussian; Smoothing; Image processing; Gaussian noise; Rician fading; Range (aeronautics); Representation (politics); Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Image restoration; Image (mathematics); Algorithm","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":[0.001441689,0.0006927143,0.0006200245,0.001845097,0.0002346129,0.0007474675,0.0005712814,0.0007770571,0.0009881096],"category_scores_gemma":[0.003473421,0.0002267107,0.0008172998,0.00070361,0.0004704274,0.0008675701,0.000672447,0.0004278045,0.0003843302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004529678,"about_ca_system_score_gemma":0.0004597856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001081439,"about_ca_topic_score_gemma":0.001176533,"domain_scores_codex":[0.9990249,0.0001795518,0.00005674017,0.0001289175,0.0005615456,0.00004840172],"domain_scores_gemma":[0.9978789,0.0005983236,0.0003474979,0.0002108936,0.0008583445,0.0001060499],"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":[0.001953462,0.0002913038,0.007281817,0.0005944297,0.0003021947,0.0003210548,0.0001850054,0.04785423,0.2725131,0.001958744,0.001443581,0.6653011],"study_design_scores_gemma":[0.00008373464,0.0009508985,0.01621854,0.00004759013,0.0002203714,0.001835149,0.00006432396,0.7766074,0.1985374,0.002648766,0.002651707,0.0001340215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08189012,0.0007603918,0.9156656,0.00008921998,0.00004649057,0.0001013035,0.0000887214,0.0006486556,0.0007094924],"genre_scores_gemma":[0.4617426,0.0007293385,0.5358989,0.00007420957,0.00007016071,0.0000562258,0.0002090475,0.0000741947,0.00114527],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001845097,"threshold_uncertainty_score":0.007624447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03329931206698302,"score_gpt":0.3443271698102279,"score_spread":0.3110278577432449,"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."}}