{"id":"W4295423199","doi":"10.1109/embc48229.2022.9871380","title":"Gradient-based Optimization Algorithm for Hybrid Loss Function in Low-dose CT Denoising","year":2022,"lang":"en","type":"article","venue":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal University Hospital; Toronto Metropolitan University","funders":"","keywords":"Noise reduction; Computer science; Artificial intelligence; Algorithm; Deep learning; Scalability; Machine learning; Pattern recognition (psychology)","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":[0.002076939,0.001105414,0.0009237266,0.0006065447,0.0003603619,0.000861276,0.001171613,0.001313623,0.001730754],"category_scores_gemma":[0.00289112,0.0003996784,0.0006077752,0.0004934425,0.0005918558,0.001080677,0.001012738,0.001825839,0.0004954696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001135427,"about_ca_system_score_gemma":0.001342132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004448962,"about_ca_topic_score_gemma":0.004572408,"domain_scores_codex":[0.9995397,0.0001366845,0.00003372438,0.00009344175,0.0001463247,0.00005012645],"domain_scores_gemma":[0.9994146,0.0002808471,0.00004898385,0.00003112775,0.0001998637,0.00002454717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001642362,0.0001103432,0.000823734,0.0001415078,0.00005573676,0.00008560256,0.0001014845,0.8201413,0.009688785,0.01202775,0.002588201,0.1540713],"study_design_scores_gemma":[0.000005745722,0.00002101656,0.00005900949,0.000006265694,0.00000437004,0.00001381865,0.000004386985,0.9972457,0.001245245,0.001040414,0.0003502404,0.000003836459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008087545,0.0002867496,0.990061,0.0001569085,0.0000263418,0.00004267533,0.00001871972,0.0002932723,0.001026717],"genre_scores_gemma":[0.3034719,0.0006152044,0.6882008,0.0003135248,0.00005576015,0.0003781517,0.0002167979,0.0003443021,0.006403526],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004448962,"threshold_uncertainty_score":0.010984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03044315820628537,"score_gpt":0.3110570622486707,"score_spread":0.2806139040423853,"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."}}