{"id":"W3031818993","doi":"10.1186/s12938-020-00778-z","title":"Clutter suppression in ultrasound: performance evaluation and review of low-rank and sparse matrix decomposition methods","year":2020,"lang":"en","type":"review","venue":"BioMedical Engineering OnLine","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Clutter; Singular value decomposition; Computer science; Robustness (evolution); Matrix decomposition; Rank (graph theory); Robust principal component analysis; Matrix completion; Artificial intelligence; Sparse matrix; Pattern recognition (psychology); Low-rank approximation; Visualization; Algorithm; Principal component analysis; Mathematics; Radar; Physics","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":[0.004881853,0.002465798,0.002288767,0.004887428,0.0005090827,0.001987679,0.001559606,0.001838727,0.002442354],"category_scores_gemma":[0.01387515,0.0007409348,0.00190934,0.005499165,0.0008936942,0.002044097,0.0009063899,0.001465312,0.001828355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006136271,"about_ca_system_score_gemma":0.001126995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004507588,"about_ca_topic_score_gemma":0.002696553,"domain_scores_codex":[0.9961697,0.0009697938,0.0004977049,0.0005814927,0.001627045,0.0001542712],"domain_scores_gemma":[0.9905878,0.006007837,0.0005560332,0.0004881741,0.002156837,0.0002031437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005221069,0.0002153292,0.002039125,0.004395652,0.0003916786,0.0001151784,0.0001234067,0.03371569,0.004661985,0.002644009,0.01167855,0.9394972],"study_design_scores_gemma":[0.0002708007,0.004263763,0.0168849,0.004132317,0.001719073,0.00280757,0.0006728158,0.7926903,0.02821687,0.01503861,0.1326736,0.0006294016],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.02122649,0.5829683,0.3837285,0.001343503,0.000821231,0.0001895598,0.0006393601,0.001671771,0.007411259],"genre_scores_gemma":[0.1492189,0.5330362,0.304116,0.001010769,0.002541227,0.0002851081,0.003725257,0.000985385,0.005081137],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004887428,"threshold_uncertainty_score":0.02581805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02286018134451782,"score_gpt":0.3909215990139938,"score_spread":0.3680614176694759,"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."}}