{"id":"W1492850407","doi":"10.1007/11866763_10","title":"Data Weighting for Principal Component Noise Reduction in Contrast Enhanced Ultrasound","year":2006,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Ultrasound and Hyperthermia Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Weighting; Principal component analysis; Imaging phantom; Ultrasound; Computer science; Noise (video); Artificial intelligence; Contrast (vision); Noise reduction; Pattern recognition (psychology); Acoustics; Optics; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001220248,0.001245834,0.0007513841,0.0009530531,0.0004399662,0.0008541771,0.0007404834,0.0007717505,0.002220412],"category_scores_gemma":[0.004928213,0.0005467998,0.0007716829,0.001260256,0.0004003999,0.001025525,0.001319475,0.001033244,0.001110973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003114574,"about_ca_system_score_gemma":0.0008088825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001602043,"about_ca_topic_score_gemma":0.001942401,"domain_scores_codex":[0.9992332,0.0002264778,0.00006985677,0.00009590268,0.0003176556,0.00005688118],"domain_scores_gemma":[0.9983819,0.0006707971,0.000102698,0.000178545,0.00061798,0.00004818769],"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":[0.0006810959,0.0001575496,0.0007820947,0.0004099869,0.00008810804,0.00004543782,0.0001288162,0.02469618,0.1414865,0.003991685,0.002226186,0.8253064],"study_design_scores_gemma":[0.00006540202,0.0001995729,0.003265181,0.00005484419,0.0001666462,0.0001910841,0.00009033177,0.8084604,0.1734331,0.004344476,0.009667696,0.0000612679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01137125,0.0005500764,0.9870777,0.00009815813,0.00006268014,0.00004375554,0.00007851901,0.0004333018,0.0002846281],"genre_scores_gemma":[0.1008982,0.0008533284,0.8953699,0.00009214321,0.00005635105,0.0001590747,0.0004993093,0.000372332,0.001699216],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002220412,"threshold_uncertainty_score":0.00742805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419601647060234,"score_gpt":0.2434735697384892,"score_spread":0.2292775532678869,"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."}}