{"id":"W6980923972","doi":"","title":"Deep Learning Methods for MRI Spinal Cord Gray Matter Segmentation","year":2019,"lang":"fr","type":"other","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Metallurgy and Cultural Artifacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Canadian Institutes of Health Research; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; National Institutes of Health; Canada First Research Excellence Fund; Nvidia","keywords":"Electrodiagnosis; Spinal cord; Syringomyelia","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004471078,0.0008998485,0.0005976355,0.001248743,0.0002886053,0.001040443,0.001030433,0.001210004,0.009474797],"category_scores_gemma":[0.00122355,0.0003664245,0.000614411,0.001075383,0.0002628035,0.0006190496,0.0009405091,0.001063001,0.004745362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008646832,"about_ca_system_score_gemma":0.00126152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01659111,"about_ca_topic_score_gemma":0.02974368,"domain_scores_codex":[0.9998204,0.00002066735,0.00001018453,0.00005435819,0.00005941674,0.00003501151],"domain_scores_gemma":[0.9996821,0.00008415878,0.00003165467,0.00004528899,0.0001269077,0.00002983513],"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":[0.00007910473,0.00007959919,0.0005889453,0.000138608,0.00006436366,0.00005962409,0.00002202146,0.05804973,0.006133032,0.006734125,0.03655815,0.8914927],"study_design_scores_gemma":[0.00001204054,0.0000235174,0.0008731541,0.0000609133,0.00002481052,0.00006862429,0.00001101138,0.9645734,0.00787014,0.01236893,0.01409878,0.0000147822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01390796,0.005483811,0.9601854,0.001260633,0.0003648088,0.00009570523,0.00223656,0.006010669,0.01045442],"genre_scores_gemma":[0.2590671,0.005138587,0.5994298,0.000872764,0.0007010202,0.0002537565,0.007222828,0.001634242,0.1256799],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01659111,"threshold_uncertainty_score":0.03298908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02249468366782667,"score_gpt":0.3236672908690627,"score_spread":0.301172607201236,"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."}}