{"id":"W4405363084","doi":"10.1109/icfsp62546.2024.10785432","title":"Visual Quality Enhancement of Low-Dose Dental CBCT Images","year":2024,"lang":"en","type":"article","venue":"","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Image enhancement; Image quality; Quality (philosophy); Artificial intelligence; Physics; Image (mathematics)","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":[0.0006268069,0.0004432664,0.0003361316,0.0008267506,0.0001583222,0.000815359,0.0003529075,0.0005744143,0.002767275],"category_scores_gemma":[0.00304912,0.0001767355,0.0003666803,0.0002564557,0.0004035588,0.0004721538,0.0006669549,0.0005673008,0.0005024806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001875235,"about_ca_system_score_gemma":0.000239896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006004408,"about_ca_topic_score_gemma":0.000668009,"domain_scores_codex":[0.9995897,0.00006739827,0.0000267069,0.00006291515,0.0002219208,0.00003142164],"domain_scores_gemma":[0.9984579,0.0006171124,0.0001860532,0.0001427186,0.0005104457,0.00008567328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008815806,0.0001265494,0.003223661,0.0009674728,0.00005290492,0.0003379009,0.0002013858,0.006781554,0.7381097,0.000478261,0.001017455,0.2478217],"study_design_scores_gemma":[0.00009623825,0.001409044,0.05703094,0.0002064425,0.0003564103,0.004884569,0.0002106765,0.1070697,0.8175031,0.001223751,0.00987945,0.0001296794],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5571007,0.003968309,0.4305359,0.000441291,0.0001704793,0.0001680475,0.0002825927,0.002004707,0.005327927],"genre_scores_gemma":[0.8485442,0.002109886,0.1444816,0.0002020154,0.000119075,0.00003497922,0.000382178,0.0002698144,0.003856272],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002767275,"threshold_uncertainty_score":0.009257436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01514331999579551,"score_gpt":0.3471004253428382,"score_spread":0.3319571053470427,"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."}}