{"id":"W2116850845","doi":"10.1109/iccv.2005.30","title":"Adaptive enhancement of cardiac magnetic resonance (CMR) images","year":2005,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Hospital for Sick Children; Canada Research Chairs","keywords":"Rician fading; Magnetic resonance imaging; Noise (video); Computer science; Attenuation; Artificial intelligence; Contrast (vision); Focus (optics); Image noise; Wavelet; Computer vision; Signal-to-noise ratio (imaging); Image (mathematics); Image contrast; SIGNAL (programming language); Pattern recognition (psychology); Physics; Algorithm; Radiology; Optics; Medicine; Fading","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003753275,0.0004338074,0.0002939958,0.0003597567,0.00009271799,0.0002853906,0.0004798714,0.0004065754,0.0006458733],"category_scores_gemma":[0.0007930407,0.000147564,0.0003718993,0.0002071378,0.00025445,0.0003364126,0.0003253744,0.0003845875,0.0003037763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122657,"about_ca_system_score_gemma":0.0001337532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002476439,"about_ca_topic_score_gemma":0.0004390328,"domain_scores_codex":[0.9998664,0.00002779951,0.000005651941,0.0000289979,0.00006045284,0.00001076323],"domain_scores_gemma":[0.9998211,0.00007340485,0.00002741237,0.00002706888,0.00003988301,0.00001111903],"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":[0.0001312887,0.00006721039,0.0006926031,0.000229235,0.00004643064,0.0002228636,0.00006335365,0.0564727,0.6241894,0.01233255,0.001028178,0.3045242],"study_design_scores_gemma":[0.00002424898,0.000389646,0.003849075,0.0000406022,0.00008988571,0.001248946,0.00002542869,0.7062321,0.2588417,0.007762339,0.02143757,0.00005859728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0214266,0.0005711653,0.9759125,0.00009814021,0.00004064539,0.00002916465,0.00002578577,0.0002867717,0.001609133],"genre_scores_gemma":[0.2355043,0.002242221,0.7580029,0.0001652132,0.0001414114,0.00004917876,0.0001185276,0.0001209407,0.003655365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006458733,"threshold_uncertainty_score":0.002160668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01717554996808883,"score_gpt":0.2657883551018115,"score_spread":0.2486128051337227,"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."}}