{"id":"W3081756191","doi":"10.1109/embc44109.2020.9176173","title":"Metal Artifacts Reduction in CT Scans using Convolutional Neural Network with Ground Truth Elimination","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Convolutional neural network; Streak; Computer science; Artificial intelligence; Ground truth; Computer vision; Image quality; Deep learning; Artifact (error); Reduction (mathematics); Pattern recognition (psychology); Image (mathematics); Geology","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":[0.0009148415,0.001245171,0.0007093769,0.001381358,0.000289063,0.0007798921,0.001037172,0.00109477,0.001009688],"category_scores_gemma":[0.002901817,0.0005702945,0.001040378,0.0007099311,0.000573653,0.0006752528,0.000791461,0.0008498932,0.0003893592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007799545,"about_ca_system_score_gemma":0.001037571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009497974,"about_ca_topic_score_gemma":0.01123352,"domain_scores_codex":[0.999513,0.00007140342,0.00003436461,0.0001296657,0.0001727048,0.00007892514],"domain_scores_gemma":[0.9991236,0.0002352101,0.0001417256,0.000192643,0.0002729697,0.00003376903],"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":[0.0007236858,0.000203124,0.005201314,0.0002212256,0.0002202318,0.0005716007,0.00009746516,0.3585851,0.0621723,0.001211627,0.003497801,0.5672946],"study_design_scores_gemma":[0.00001443495,0.00005731701,0.001797478,0.00001307088,0.00005291068,0.0001824248,0.00001053392,0.9747109,0.02181429,0.0007244272,0.0006089082,0.00001323914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1569292,0.001029574,0.8357627,0.0003768959,0.00008832313,0.0001015611,0.0003609064,0.003856392,0.001494508],"genre_scores_gemma":[0.7204401,0.0005226755,0.2744395,0.0002492571,0.00004849257,0.00006003094,0.001383183,0.0003023681,0.002554348],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009497974,"threshold_uncertainty_score":0.01888537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02311827934937498,"score_gpt":0.2200895233081459,"score_spread":0.1969712439587709,"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."}}