{"id":"W2281620867","doi":"10.1186/s12880-016-0112-5","title":"A sinogram denoising algorithm for low-dose computed tomography","year":2016,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"TRIUMF; University of British Columbia Hospital","funders":"University of British Columbia","keywords":"Noise reduction; Noise (video); Computer science; Projection (relational algebra); Algorithm; Shot noise; Image quality; Artificial intelligence; Iterative reconstruction; Computer vision; Image (mathematics); Detector","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":[0.001143726,0.0006734225,0.0007313504,0.0008606357,0.000356311,0.0006476741,0.0008594093,0.001084714,0.001911902],"category_scores_gemma":[0.003049518,0.0003727469,0.000788545,0.0009666409,0.000662519,0.0007421616,0.0007303297,0.001355301,0.0009644378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004031922,"about_ca_system_score_gemma":0.00100929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001705766,"about_ca_topic_score_gemma":0.002267887,"domain_scores_codex":[0.9994577,0.0001491678,0.00003613229,0.00007633966,0.0002590043,0.00002166307],"domain_scores_gemma":[0.9989471,0.0004798014,0.00009480026,0.0001153433,0.0003243379,0.00003860454],"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":[0.0003539405,0.00008751374,0.001400127,0.0002301631,0.000103732,0.0001669926,0.0001607767,0.2855498,0.03989081,0.01682565,0.003383267,0.6518472],"study_design_scores_gemma":[0.00001607759,0.00003150325,0.0002931305,0.0000117893,0.00001415982,0.000110322,0.00001128855,0.9858829,0.007969867,0.003377343,0.002271141,0.00001042839],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003729649,0.0001513608,0.9953845,0.00006846521,0.00001971136,0.00001731425,0.00001837469,0.0002576483,0.0003529122],"genre_scores_gemma":[0.05844847,0.0003862702,0.9391184,0.0000696541,0.00005163483,0.00008539898,0.0002234946,0.0001695225,0.00144706],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001911902,"threshold_uncertainty_score":0.006395996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02140962268604206,"score_gpt":0.3284569278973161,"score_spread":0.307047305211274,"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."}}