{"id":"W2896950961","doi":"10.1016/j.compbiomed.2019.103335","title":"Fully-automated tongue detection in ultrasound images","year":2019,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre for Research on Brain Language and Music; Centre Hospitalier Universitaire Sainte-Justine; Université du Québec à Montréal; École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Initialization; Computer science; Artificial intelligence; Computer vision; Tracking (education); Skeletonization; Segmentation; Image segmentation; Pattern recognition (psychology)","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":[],"consensus_categories":[],"category_scores_codex":[0.0003031293,0.0001566608,0.0004897017,0.0003618666,0.00002725156,0.00000151627,0.00005211828,0.0001053961,0.00004563719],"category_scores_gemma":[0.0002300326,0.0001090946,0.00002102644,0.0002882438,0.0003449192,0.00003576803,0.00002938711,0.0002816535,0.0000116965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005608053,"about_ca_system_score_gemma":0.0000210214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001216982,"about_ca_topic_score_gemma":0.00007050503,"domain_scores_codex":[0.9990465,0.00006433221,0.0002907789,0.0003017987,0.00008379589,0.0002127807],"domain_scores_gemma":[0.9992284,0.0004781615,0.00004764206,0.0001388071,0.00003750544,0.0000695491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003090925,0.0001068184,0.9161761,0.0001838587,0.00006195013,0.0001104383,0.000819323,0.000008727818,0.07230259,0.0007794765,0.001649588,0.007491998],"study_design_scores_gemma":[0.00479896,0.0009741092,0.9899955,0.0004730074,0.0000215954,0.0004152625,0.0002203686,0.0005809237,0.0004329243,0.001343314,0.0006391262,0.0001048597],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909481,0.001836745,0.0002798873,0.003204585,0.001008999,0.0003526542,0.000002581179,0.0001193283,0.002247148],"genre_scores_gemma":[0.9975253,0.0004141066,0.0004273426,0.001192924,0.0003100445,0.00001246097,0.0000505865,0.00000876324,0.00005850103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07381941,"threshold_uncertainty_score":0.4448747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01057339750299531,"score_gpt":0.3103769166032697,"score_spread":0.2998035191002744,"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."}}