{"id":"W2959573983","doi":"10.1109/isbi.2019.8759216","title":"Multi-Focus Ultrasound Imaging Using Generative Adversarial Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Frame rate; Computer science; Imaging phantom; Focus (optics); Frame (networking); Artificial intelligence; Deep learning; Generative adversarial network; Adversarial system; Ultrasound imaging; Computer vision; Generative grammar; Reduction (mathematics); Image resolution; Line (geometry); Image (mathematics); Ultrasound; Optics; Acoustics; Mathematics; Telecommunications","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":[0.0001123801,0.0002065451,0.0001928706,0.00006006614,0.00007211364,0.00007335658,0.0001283901,0.00005463609,0.00049568],"category_scores_gemma":[0.0000193602,0.0001997341,0.00006846076,0.0001368363,0.00003127502,0.0002663908,0.00002399082,0.0002278659,0.00007684023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001353074,"about_ca_system_score_gemma":0.00002505148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001233803,"about_ca_topic_score_gemma":0.000009127942,"domain_scores_codex":[0.9990067,0.00001792465,0.000194072,0.0002217253,0.0001216426,0.0004379583],"domain_scores_gemma":[0.9995446,0.0001096277,0.00002398955,0.0002080655,0.00003196974,0.00008171969],"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":[0.000005053228,0.00001366221,0.003590255,0.00001096427,0.00005064074,0.000008534631,0.0002240293,0.8061962,0.1882863,0.00007528166,0.0005018929,0.001037193],"study_design_scores_gemma":[0.0007733282,0.00000362099,0.0004615863,0.00002024812,0.00002564642,0.00004347992,0.0002758509,0.9909706,0.006797956,0.00002060166,0.0003199684,0.0002870961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1321066,0.0003955412,0.8565232,0.000008870205,0.001577453,0.0002079422,0.000006098464,0.0003931181,0.008781169],"genre_scores_gemma":[0.968882,0.00002134639,0.03033554,0.0001029628,0.000291533,0.000003649365,0.000007123066,0.00004996869,0.000305903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8367754,"threshold_uncertainty_score":0.814492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007982868099269683,"score_gpt":0.2125345966787634,"score_spread":0.2045517285794937,"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."}}