{"id":"W4224088275","doi":"10.3390/tomography8020091","title":"Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study","year":2022,"lang":"en","type":"article","venue":"Tomography","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Lawson Health Research Institute; BC Cancer Agency; University Health Network; University of Toronto; University of British Columbia","funders":"Canadian Institutes of Health Research; National Cancer Institute; National Institutes of Health; Ontario Institute for Cancer Research; University of Iowa; Geoffrey Beene Cancer Research Center; Memorial Sloan-Kettering Cancer Center","keywords":"Repeatability; Imaging phantom; Reproducibility; Artificial intelligence; Scanner; Robustness (evolution); Feature (linguistics); Pattern recognition (psychology); Noise (video); Image noise; Intraclass correlation; Computer science; Computer vision; Image texture; Image processing; Mathematics; Image (mathematics); Nuclear medicine; Statistics; Medicine","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":[0.005931455,0.0005631383,0.0006732714,0.0008113381,0.0002794365,0.0006692306,0.0006495338,0.0006267557,0.0006526862],"category_scores_gemma":[0.01607453,0.0004397442,0.0005484366,0.0005981657,0.0006883292,0.0005541138,0.0008962969,0.0003129874,0.0002030789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003298432,"about_ca_system_score_gemma":0.0002474774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004780587,"about_ca_topic_score_gemma":0.0004418001,"domain_scores_codex":[0.996065,0.001978779,0.0002722132,0.0008438494,0.0006964394,0.0001437099],"domain_scores_gemma":[0.9920908,0.003916154,0.0008548519,0.002055631,0.0009804068,0.0001021701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003667346,0.0007706054,0.02967293,0.000406475,0.0006213924,0.0002540426,0.0006597331,0.04084647,0.8049883,0.0004549964,0.0007239365,0.1169337],"study_design_scores_gemma":[0.0001907898,0.007021153,0.1787903,0.00003943805,0.0007569962,0.001810375,0.0001652586,0.1425862,0.663548,0.0009957572,0.003913397,0.0001823761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8601262,0.0005519415,0.1374432,0.00009085123,0.00003246746,0.0002163584,0.0002074578,0.0007467914,0.0005846855],"genre_scores_gemma":[0.9453081,0.0001062756,0.05342245,0.0000621198,0.00002432646,0.0001652555,0.0003833839,0.0002609407,0.0002672421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005931455,"threshold_uncertainty_score":0.03136891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00931209864084437,"score_gpt":0.2763780063939373,"score_spread":0.2670659077530929,"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."}}