{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008775933,0.0005647579,0.0005656392,0.0007330093,0.0002703843,0.00052568,0.0004713866,0.0008361358,0.001033462],"category_scores_gemma":[0.001463268,0.0002312285,0.0005843321,0.0006498716,0.0003672075,0.0006417359,0.0004660706,0.0007671001,0.0003807121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002941786,"about_ca_system_score_gemma":0.0004031263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001069123,"about_ca_topic_score_gemma":0.001664647,"domain_scores_codex":[0.9997388,0.00004221686,0.00001550005,0.00005980333,0.0001141187,0.00002954125],"domain_scores_gemma":[0.9996226,0.0001168093,0.00004330448,0.00006769455,0.0001271819,0.00002253495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003122419,0.0001740203,0.001323313,0.0002581693,0.00008703468,0.0003230285,0.00023068,0.2328582,0.3281531,0.02723539,0.003189566,0.4058552],"study_design_scores_gemma":[0.000006788357,0.0000552692,0.0007148952,0.000009415214,0.00001642847,0.0001466364,0.00001613498,0.931447,0.06131925,0.00349252,0.002760645,0.00001496943],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0254558,0.0001819739,0.9729854,0.00009552621,0.00007401177,0.0000215883,0.00005782531,0.0001636717,0.0009641745],"genre_scores_gemma":[0.2745582,0.000670933,0.7198093,0.00008994877,0.0001066717,0.00005355129,0.0002979395,0.00009608496,0.004317451],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001069123,"threshold_uncertainty_score":0.004641175,"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."}}