{"id":"W2984794381","doi":"10.1121/1.5136929","title":"A convolutional neural network approach to preserve image quality for sparse array data","year":2019,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Interpolation (computer graphics); Image quality; Channel (broadcasting); Imaging phantom; Computer vision; Bandwidth (computing); Frame rate; Sparse array; Image (mathematics); Algorithm; Telecommunications; Optics; Physics","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":[0.0005681528,0.0006413128,0.0003178441,0.0003598277,0.0001956684,0.0004618774,0.0007750305,0.0005664312,0.001544807],"category_scores_gemma":[0.001442853,0.0002302943,0.0003211064,0.00042142,0.0003634173,0.0007197229,0.0006640134,0.0009403563,0.0003295465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007151088,"about_ca_system_score_gemma":0.0007251663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005622094,"about_ca_topic_score_gemma":0.008490046,"domain_scores_codex":[0.9998223,0.0000237549,0.000009562272,0.00004133883,0.00007124823,0.00003180464],"domain_scores_gemma":[0.9996387,0.00009867117,0.00003931705,0.00007131079,0.0001347143,0.0000173394],"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":[0.0002258616,0.0001760652,0.001444526,0.00009539049,0.0001177263,0.0001231463,0.00007425251,0.502502,0.1195962,0.006761874,0.003302282,0.3655807],"study_design_scores_gemma":[0.000003846599,0.00003740769,0.0003013343,0.000003382371,0.000009283129,0.00002513458,0.000003474682,0.9845479,0.01348538,0.0008229892,0.0007546266,0.000005151945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05246693,0.0001872927,0.9439691,0.0002422758,0.00004382727,0.00003563645,0.0001622899,0.001069929,0.001822705],"genre_scores_gemma":[0.5889253,0.0003048903,0.4013186,0.0002764883,0.00005353595,0.00009253994,0.0006044104,0.0001753326,0.008248943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005622094,"threshold_uncertainty_score":0.01117873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03966751297451924,"score_gpt":0.3106628819892077,"score_spread":0.2709953690146885,"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."}}