{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005256467,0.0003288851,0.0002547122,0.0002818257,0.00009946107,0.0002284675,0.0003215668,0.0003572195,0.001276959],"category_scores_gemma":[0.0002832245,0.0003011149,0.0001983405,0.0002247804,0.0002277447,0.0003995251,0.0004132511,0.0003850708,0.0003161875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000140737,"about_ca_system_score_gemma":0.0002283999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003727229,"about_ca_topic_score_gemma":0.000628321,"domain_scores_codex":[0.9998385,0.00003481506,0.00000849544,0.00005215068,0.00004234559,0.00002375517],"domain_scores_gemma":[0.999872,0.00003943856,0.00002584043,0.00002625845,0.00001766518,0.00001880514],"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":[0.00007773303,0.0000126718,0.0001558273,0.00002847099,0.000004159168,0.00004476233,0.00001802843,0.0004148967,0.9937563,0.0003011281,0.00006355691,0.005122555],"study_design_scores_gemma":[0.00001481561,0.0004217052,0.003508665,0.000007467852,0.0000150509,0.0005528821,0.00001326823,0.01391593,0.9785638,0.0002082534,0.002759201,0.00001900027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4390562,0.0009330116,0.5554553,0.0001894454,0.00003468301,0.0001966096,0.0004782192,0.00113083,0.002525616],"genre_scores_gemma":[0.4231196,0.0008935361,0.5703608,0.0001001641,0.00001845344,0.0002272199,0.0004357446,0.0001349402,0.004709611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001276959,"threshold_uncertainty_score":0.004271865,"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."}}