{"id":"W1925068057","doi":"10.1007/s11548-015-1297-8","title":"Adaptive noise correction of dual-energy computed tomography images","year":2015,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Noise reduction; Noise (video); Artificial intelligence; Computer science; Computer vision; Image noise; Monochromatic color; Filter (signal processing); Image quality; Wavelet; Median filter; Anisotropic diffusion; Image processing; Optics; Image (mathematics); Physics","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":[0.0002556644,0.0001142955,0.00032461,0.0004333774,0.00001995319,0.00001593126,0.0001054417,0.00006715709,0.000003998942],"category_scores_gemma":[0.00002516588,0.0001046454,0.0001342104,0.0001212288,0.00009877047,0.000242461,0.00002511338,0.0001703351,4.510005e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003622509,"about_ca_system_score_gemma":0.00003880989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004419663,"about_ca_topic_score_gemma":8.597583e-7,"domain_scores_codex":[0.9991039,0.00008596048,0.0004478322,0.00008266521,0.0001682474,0.0001113893],"domain_scores_gemma":[0.9987886,0.0004110582,0.000213728,0.00005520381,0.0004437531,0.0000876755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001058137,0.0002853937,0.04964444,0.0000317893,0.002937004,0.0009624088,0.000765536,0.364629,0.003596933,0.0006259731,0.07049756,0.5049658],"study_design_scores_gemma":[0.003748789,0.00066735,0.2885751,0.0008207361,0.0001848068,0.01923876,0.0004320244,0.6526253,0.01108599,0.003253194,0.01841439,0.0009534928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1755025,0.002654854,0.8116039,0.0001644899,0.00957571,0.00002276822,0.000007089917,0.00005109401,0.0004175422],"genre_scores_gemma":[0.994172,0.0001717586,0.004971351,0.0001060821,0.0005495818,8.144575e-7,0.000008185776,0.00001016411,0.00001004892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8186695,"threshold_uncertainty_score":0.4267317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137105864548425,"score_gpt":0.2248660746755227,"score_spread":0.2111554882206802,"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."}}