{"id":"W2137969878","doi":"10.1109/tmi.2004.832656","title":"A Method for Modeling Noise in Medical Images","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":308,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Gaussian noise; Noise (video); Shot noise; Image noise; Computer science; Artificial intelligence; Quantization (signal processing); Medical imaging; Computer vision; Pattern recognition (psychology); Image (mathematics)","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":[0.001283483,0.0002337131,0.0004333483,0.0003236459,0.0001483977,0.00002498399,0.0002693189,0.0002067923,0.000582148],"category_scores_gemma":[0.0002889219,0.0002028608,0.0002277676,0.0004270768,0.0001880017,0.0001125935,0.000004114201,0.001089368,0.0000297641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001805557,"about_ca_system_score_gemma":0.000605394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003203571,"about_ca_topic_score_gemma":0.00003015181,"domain_scores_codex":[0.9971309,0.00005105866,0.0006167915,0.0005257417,0.001163702,0.0005118262],"domain_scores_gemma":[0.9983034,0.0003100508,0.00004699365,0.0003715354,0.0001102355,0.0008578244],"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":[0.0003838434,0.003615674,0.00009195552,0.0004512329,0.0001149521,0.0007747482,0.000506054,0.007958914,0.0130608,0.001744188,0.003165559,0.9681321],"study_design_scores_gemma":[0.004988086,0.00008724496,0.00001558688,0.001298738,0.0001115602,0.0005823035,0.000128763,0.9713154,0.0156042,0.003383161,0.002198981,0.0002859904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002808396,0.00007254415,0.9050611,0.09049619,0.0001585523,0.000647706,0.00001211393,0.0003296392,0.000413738],"genre_scores_gemma":[0.697288,0.0002228428,0.2914884,0.01001893,0.0001985136,0.0006073292,0.00001189569,0.00005825318,0.0001058967],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9678461,"threshold_uncertainty_score":0.8272422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02836811517753919,"score_gpt":0.3791660219313389,"score_spread":0.3507979067537997,"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."}}