{"id":"W4310584371","doi":"10.1109/ius54386.2022.9958236","title":"In Vivo Super Resolution Ultrasound Imaging using the Erythrocytes - SURE","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Ultrasonics Symposium (IUS)","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Compute Canada","funders":"H2020 European Research Council","keywords":"In vivo; Ultrasound; Resolution (logic); Ultrasonic imaging; Ultrasound imaging; Computer science; Image resolution; Preclinical imaging; Superresolution; Biomedical engineering; Computer vision; Artificial intelligence; Radiology; Medicine; Image (mathematics); Biology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008830248,0.0003218795,0.0003197571,0.0005097631,0.000620707,0.0001437182,0.0005572266,0.00006333002,0.00188985],"category_scores_gemma":[0.0001800084,0.0002896486,0.0003066223,0.0008309299,0.0002128523,0.0004134325,0.0001124251,0.001047205,0.00001791643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008606692,"about_ca_system_score_gemma":0.0002252951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008264644,"about_ca_topic_score_gemma":0.00005899068,"domain_scores_codex":[0.9968626,0.0002066825,0.0005658884,0.0006062666,0.00118432,0.0005742792],"domain_scores_gemma":[0.9984512,0.0005887192,0.0001804819,0.0004804139,0.0001803787,0.0001187876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002529486,0.0005097083,0.1733003,0.00002729928,0.0002376795,0.00006474151,0.001886473,0.02113782,0.7808165,0.0008623517,0.0208638,0.0000403709],"study_design_scores_gemma":[0.00717688,0.0004471524,0.01864701,0.0003333857,0.0006430151,0.01471322,0.01278632,0.09167295,0.02765054,0.001487457,0.8225856,0.001856421],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744552,0.001029633,0.001657363,0.01051278,0.005089089,0.0005640163,0.0003055869,0.00015148,0.006234793],"genre_scores_gemma":[0.9926142,0.0004442358,0.001559598,0.002702475,0.000443379,0.0001099634,0.0001174243,0.00007656417,0.001932169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8017219,"threshold_uncertainty_score":0.9999556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01286812095110793,"score_gpt":0.2713899400729725,"score_spread":0.2585218191218646,"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."}}