{"id":"W3006913085","doi":"10.1109/jbhi.2020.2977224","title":"Multiple Axial Spine Indices Estimation via Dense Enhancing Network With Cross-Space Distance-Preserving Regularization","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Discriminative model; Artificial intelligence; Regularization (linguistics); Computer science; Embedding; Ground truth; Feature vector; Deep learning; Pattern recognition (psychology); Feature (linguistics); Block (permutation group theory); Cross-validation; Machine learning; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0006809438,0.0001183734,0.0003302549,0.0000822923,0.0001389551,0.00008021568,0.0001131833,0.00007200029,0.00001268699],"category_scores_gemma":[0.0001360584,0.0000847203,0.00003718075,0.0003618065,0.00009697861,0.0003909806,0.00001771172,0.0003432697,0.000002238094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004648575,"about_ca_system_score_gemma":0.00008831739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007291401,"about_ca_topic_score_gemma":0.000007150833,"domain_scores_codex":[0.9981942,0.00002127297,0.0009903477,0.00005007497,0.0004873542,0.0002567924],"domain_scores_gemma":[0.9987968,0.00005306709,0.0004376082,0.00006560169,0.00008411999,0.0005627642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001373278,0.00009874917,0.01829485,0.009541921,0.0004420258,0.00007648562,0.0198907,0.7259417,0.0005788816,0.00009236192,0.01541951,0.2094855],"study_design_scores_gemma":[0.0007149501,0.0001770358,0.001121545,0.0005082892,0.00002800519,0.00004857625,0.0001617617,0.9943208,0.0001135048,0.000070396,0.002636305,0.00009879756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08362552,0.0004344287,0.91278,0.00282087,0.0002118381,0.00005953619,0.000002838476,0.00004020704,0.0000247663],"genre_scores_gemma":[0.9255839,0.000474429,0.07213951,0.0009468605,0.0008150848,0.000001198442,0.00001694313,0.00001373411,0.000008320892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8419584,"threshold_uncertainty_score":0.3454794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456347641858164,"score_gpt":0.2685712723463302,"score_spread":0.2540077959277486,"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."}}