{"id":"W2031628593","doi":"10.1118/1.4801905","title":"Anatomical noise in contrast‐enhanced digital mammography. Part I. Single‐energy imaging","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Ontario Tech University; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Mammography; Digital mammography; Medical imaging; Contrast (vision); Noise (video); Medical physics; Energy (signal processing); Contrast-to-noise ratio; Physics; Computer science; Medicine; Optics; Radiology; Computer vision; Image quality; Breast cancer; Cancer","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":[0.00009345078,0.0002580904,0.0004521582,0.000157751,0.00003820167,0.0001399565,0.0001813191,0.00008791636,0.0002236832],"category_scores_gemma":[0.0001536895,0.0002201124,0.0002705578,0.0007316315,0.0004413855,0.000682188,0.00007020502,0.0003523718,0.00007619998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004912788,"about_ca_system_score_gemma":0.00009270035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007202116,"about_ca_topic_score_gemma":0.000004203464,"domain_scores_codex":[0.9977819,0.00002094482,0.0004228618,0.0004033026,0.000758931,0.0006120392],"domain_scores_gemma":[0.9988099,0.0001204754,0.00006909161,0.0002796366,0.00009425219,0.000626607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006753024,0.001387906,0.0470016,0.00004432952,0.00006863808,0.0001951562,0.00006924447,0.000001161571,0.002397667,0.0007350299,0.003791418,0.9442403],"study_design_scores_gemma":[0.05672078,0.002372895,0.4764242,0.01011307,0.0007567236,0.002298641,0.002011857,0.02735548,0.1396404,0.2050463,0.06959027,0.007669338],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9148533,0.0003961535,0.01715952,0.004333138,0.0004474043,0.0003749646,0.0000219556,0.0002925922,0.06212092],"genre_scores_gemma":[0.9969563,0.00001804102,0.00004400049,0.002203186,0.0005225078,0.00004164485,0.00007353241,0.00003676786,0.0001040393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.936571,"threshold_uncertainty_score":0.8975924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006298797560840437,"score_gpt":0.2180681929858994,"score_spread":0.211769395425059,"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."}}