{"id":"W2792826991","doi":"10.1109/tmi.2018.2811483","title":"A Dual Tissue-Doppler Optical-Flow Method for Speckle Tracking Echocardiography at High Frame Rate","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Speckle pattern; Doppler effect; Tracking (education); Frame (networking); Computer science; Computer vision; Artificial intelligence; Physics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008119209,0.0003628286,0.0004304262,0.0002852641,0.0003947717,0.0000937123,0.0002412078,0.0001640641,0.001142096],"category_scores_gemma":[0.00007989742,0.0003598918,0.0002685707,0.0004314244,0.0002922113,0.0002469236,0.000003545598,0.0006679478,0.0001260692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001654657,"about_ca_system_score_gemma":0.00005395328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003525308,"about_ca_topic_score_gemma":0.0000147921,"domain_scores_codex":[0.9976246,0.000070108,0.0004481898,0.0004952645,0.0005487798,0.0008130985],"domain_scores_gemma":[0.9982317,0.0008089108,0.00003783499,0.0003697791,0.0001095494,0.0004422287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001712478,0.000248309,0.00003872552,0.0002561958,0.0006527605,0.0001906995,0.00107394,0.05534771,0.1140642,0.0001315828,0.01341056,0.8144141],"study_design_scores_gemma":[0.001241672,0.00005602173,0.00007949315,0.0001675166,0.0002166618,0.0001904661,0.0001127949,0.7443267,0.2452694,0.0003573497,0.007497278,0.00048473],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004575696,0.00009383572,0.9897385,0.0008410622,0.002666351,0.0003651348,0.00008700261,0.0007218749,0.0009105857],"genre_scores_gemma":[0.8808695,0.00007411545,0.1169806,0.0008690808,0.0007714155,0.0001268953,0.00001131433,0.0001234328,0.0001736683],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8762938,"threshold_uncertainty_score":0.9998853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009672218624067318,"score_gpt":0.2691860399079277,"score_spread":0.2595138212838604,"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."}}