{"id":"W2659398814","doi":"10.1109/isbi.2017.7950713","title":"Detection of lumen and media-adventitia borders in IVUS images using sparse auto-encoder neural network","year":2017,"lang":"en","type":"article","venue":"","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Softmax function; Artificial intelligence; Computer science; Lumen (anatomy); Pattern recognition (psychology); Artificial neural network; Intravascular ultrasound; Computer vision; Feature (linguistics); Radiology","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":[0.0004317385,0.0003722759,0.0003467704,0.0005609799,0.0001371605,0.0003376892,0.0003651672,0.0005242274,0.0004722135],"category_scores_gemma":[0.001243453,0.0002475969,0.0002461253,0.0002595232,0.0001674855,0.0004163407,0.0002813819,0.0002990978,0.0001807302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000229187,"about_ca_system_score_gemma":0.0002464816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00189194,"about_ca_topic_score_gemma":0.003334816,"domain_scores_codex":[0.9998317,0.00003454245,0.00001156684,0.00004450175,0.00006127745,0.000016464],"domain_scores_gemma":[0.9995881,0.0001850962,0.00005988916,0.00003447815,0.0001173901,0.0000150665],"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":[0.0003486295,0.000147368,0.01193215,0.0001766378,0.00007094879,0.0002943061,0.0001802498,0.09751432,0.1658858,0.001026707,0.001177631,0.7212453],"study_design_scores_gemma":[0.000005996367,0.00005307213,0.005907012,0.00001186003,0.00002278949,0.0002165327,0.00001690877,0.9695902,0.02337534,0.0003569105,0.0004288366,0.0000145278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1789116,0.0004421581,0.8183902,0.0000968583,0.0000331208,0.00004963228,0.00009315571,0.001066714,0.0009165276],"genre_scores_gemma":[0.6334786,0.0003251104,0.3643623,0.00008407976,0.00003014941,0.00006087143,0.0001627991,0.00004842405,0.001447709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00189194,"threshold_uncertainty_score":0.003761768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03240963069613959,"score_gpt":0.3247099884013294,"score_spread":0.2923003577051898,"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."}}