{"id":"W2789255769","doi":"10.1109/trpms.2018.2810221","title":"Denoising Low-Dose CT Images Using Multiframe Blind Source Separation and Block Matching Filter","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Radiation and Plasma Medical Sciences","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; University of Saskatchewan","keywords":"Artificial intelligence; Noise reduction; Image quality; Computer vision; Wiener filter; Filter (signal processing); Computer science; Noise (video); Optical transfer function; Image resolution; Pattern recognition (psychology); Image restoration; Mathematics; Image (mathematics); Image processing","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":[0.001052795,0.000814009,0.0006216872,0.0009818873,0.0002146389,0.0004863787,0.0005850301,0.001099583,0.001068384],"category_scores_gemma":[0.001870537,0.0003078516,0.0008787823,0.0006830505,0.0003570962,0.0009325276,0.000467683,0.0006870211,0.0004804106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00047815,"about_ca_system_score_gemma":0.0006182601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001815504,"about_ca_topic_score_gemma":0.001901916,"domain_scores_codex":[0.9996411,0.00007218321,0.00003032262,0.00007276794,0.0001621108,0.00002166283],"domain_scores_gemma":[0.9994214,0.0002233802,0.00009254918,0.00006236196,0.0001758093,0.00002454939],"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":[0.0005436357,0.0001611175,0.001183008,0.0003704722,0.0001415229,0.0002019,0.000200201,0.1305673,0.5342837,0.006226757,0.000901509,0.3252188],"study_design_scores_gemma":[0.00002355633,0.0001589606,0.001034441,0.00001733361,0.00005346842,0.0002671311,0.00001800742,0.836452,0.1578663,0.002054036,0.002010154,0.00004455748],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01282123,0.0001516965,0.9864162,0.0000496858,0.00001948402,0.00002680195,0.00002310344,0.0002672993,0.000224435],"genre_scores_gemma":[0.1160472,0.000313329,0.8820659,0.00006123143,0.00002670395,0.00007609743,0.000117524,0.00007823632,0.001213812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001815504,"threshold_uncertainty_score":0.005567789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03739814363690503,"score_gpt":0.3326733678749944,"score_spread":0.2952752242380894,"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."}}