{"id":"W2914969418","doi":"10.1371/journal.pone.0211621","title":"Multi-channel framelet denoising of diffusion-weighted images","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute on Aging; National Institutes of Health; National Natural Science Foundation of China; Foundation for the National Institutes of Health","keywords":"Noise reduction; Piecewise; Thresholding; Computer science; SIGNAL (programming language); Artificial intelligence; Attenuation; Wavelet; Algorithm; Noise (video); Total variation denoising; Mathematics; Pattern recognition (psychology); Image (mathematics); Physics; Mathematical analysis; Optics","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":[0.0003414145,0.0001378332,0.0003258266,0.0001418047,0.00006579926,0.00008503052,0.0006618806,0.00007724114,0.0000505075],"category_scores_gemma":[0.000110653,0.0001239407,0.00007148633,0.000323638,0.0000402218,0.0003696377,0.0002827043,0.0001717929,0.0001688933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001895841,"about_ca_system_score_gemma":0.00003391947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003686973,"about_ca_topic_score_gemma":6.102982e-7,"domain_scores_codex":[0.998567,0.0001355283,0.0002567873,0.0003368917,0.0004366535,0.0002671454],"domain_scores_gemma":[0.9987602,0.0002244839,0.0001254622,0.0006279898,0.0001886774,0.00007323808],"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":[0.00001710999,0.001017806,0.0006326671,0.0001016645,0.00006379137,0.00001404203,0.0006267367,0.00001098662,0.994256,0.0002193729,0.00006448971,0.002975355],"study_design_scores_gemma":[0.001281586,0.0001486143,0.003848708,0.0003240302,0.00003320331,0.000003782535,0.00002326615,0.0948434,0.8971812,0.002025419,0.00002738082,0.0002593878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4699939,0.0002557455,0.5287421,0.000183635,0.0001008894,0.0001636872,0.000002706187,0.00009783229,0.0004595783],"genre_scores_gemma":[0.5531343,0.00002225202,0.445351,0.0002050452,0.00004110526,0.000003478826,0.000001717066,0.00001360723,0.001227545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09707475,"threshold_uncertainty_score":0.5054156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04021998101577932,"score_gpt":0.2549633454357967,"score_spread":0.2147433644200174,"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."}}