{"id":"W4387211254","doi":"10.1007/978-3-031-43901-8_22","title":"EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"","keywords":"Landmark; Computer science; Smoothing; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Graph; Machine learning; Computer vision; Theoretical computer science","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"],"consensus_categories":[],"category_scores_codex":[0.0005992215,0.0005408434,0.0005048349,0.0008336963,0.0005401518,0.0004006333,0.002592098,0.0003667074,0.000002185736],"category_scores_gemma":[0.0000869881,0.0005081053,0.0003137827,0.001322892,0.0004255717,0.0003563593,0.0007761797,0.001171123,0.0000255895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002233956,"about_ca_system_score_gemma":0.00008411318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004887695,"about_ca_topic_score_gemma":0.00005846404,"domain_scores_codex":[0.9959236,0.00004456508,0.0004977513,0.00184851,0.0007568711,0.0009287422],"domain_scores_gemma":[0.996398,0.001485514,0.0002521386,0.001468325,0.0001618652,0.0002342074],"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":[0.00001358838,0.00001309515,0.00001495622,0.000007822507,0.000007801255,0.00001752811,0.00003932351,0.4415217,0.00002287122,0.005618934,0.00002311802,0.5526993],"study_design_scores_gemma":[0.000262688,0.0002817618,0.0001493452,0.00007925191,0.000008615382,0.00003822276,3.853195e-8,0.8311473,0.0003367695,0.1660611,0.001179853,0.0004550911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00008316443,0.00009705868,0.9945664,0.0007546642,0.002588199,0.001053744,0.00000900005,0.0005962356,0.0002515515],"genre_scores_gemma":[0.742178,0.0001944653,0.2509117,0.003005468,0.002806563,0.0002092418,0.00004356499,0.0001843456,0.0004666793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7436547,"threshold_uncertainty_score":0.9997371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01763311846465119,"score_gpt":0.2552607214604523,"score_spread":0.2376276029958011,"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."}}