{"id":"W2923494043","doi":"10.1016/j.compbiomed.2019.03.015","title":"A multi-scale data fusion framework for bone age assessment with convolutional neural networks","year":2019,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Deep learning; Feature extraction; Contourlet; Domain (mathematical analysis); Feature (linguistics); Data set; Machine learning; Scale (ratio); Artificial neural network; Data mining; Mathematics; Cartography; Geography","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":[0.001285073,0.00109525,0.0013081,0.001458984,0.0004812518,0.0008373883,0.001681109,0.001499349,0.001462576],"category_scores_gemma":[0.001335899,0.0005300324,0.001422406,0.001306416,0.0003167116,0.0009995031,0.001724469,0.00127257,0.0008279477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007394162,"about_ca_system_score_gemma":0.001041153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01835818,"about_ca_topic_score_gemma":0.02170691,"domain_scores_codex":[0.9995474,0.00005672883,0.00003042345,0.00014374,0.0001368175,0.00008489619],"domain_scores_gemma":[0.9995977,0.00009563129,0.00004083791,0.00004335985,0.0001944616,0.00002797118],"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":[0.0003110427,0.0002155338,0.002560974,0.00009190557,0.0002603536,0.000143252,0.00006038707,0.255309,0.01667779,0.002989304,0.004147085,0.7172334],"study_design_scores_gemma":[0.000004836202,0.00002582366,0.0006409069,0.000006560201,0.00003348407,0.00003278706,0.000006748695,0.9945627,0.002620585,0.001478892,0.0005771763,0.000009516967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02006876,0.001107491,0.9758827,0.0002434443,0.00009946233,0.00004672566,0.0003044659,0.001408226,0.0008387467],"genre_scores_gemma":[0.5505656,0.0009953787,0.4409768,0.0003372214,0.000222631,0.0001522663,0.001366553,0.0001690969,0.005214422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01835818,"threshold_uncertainty_score":0.0365026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02907664103159235,"score_gpt":0.3550725002872839,"score_spread":0.3259958592556915,"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."}}