{"id":"W2438513049","doi":"10.1109/isbi.2016.7493414","title":"A sparse coding approach for the efficient representation and segmentation of white matter fibers","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Embedding; Computer science; Neural coding; Segmentation; Artificial intelligence; Pattern recognition (psychology); Sparse approximation; Pairwise comparison; Centroid; Representation (politics); Fiber bundle; Coding (social sciences); Fiber; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006512043,0.0006412905,0.0007373681,0.001386656,0.0004191406,0.0007201229,0.001047814,0.0009917425,0.001587984],"category_scores_gemma":[0.002130599,0.0004372615,0.000751248,0.001638646,0.0006847232,0.001164945,0.001150022,0.001643425,0.0007657449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004481936,"about_ca_system_score_gemma":0.001146617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003732639,"about_ca_topic_score_gemma":0.003870294,"domain_scores_codex":[0.9995944,0.00008552836,0.00002445311,0.00007434604,0.0001780873,0.00004314497],"domain_scores_gemma":[0.9993154,0.0002866237,0.00008155325,0.00009519785,0.0001827409,0.00003849016],"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":[0.0001174227,0.0001031851,0.0008133929,0.0002703651,0.00007774743,0.0002091081,0.0002973218,0.2370533,0.0925856,0.06767937,0.00827783,0.5925153],"study_design_scores_gemma":[0.0000094593,0.00005213427,0.0003486249,0.00001895232,0.00001230713,0.0001649896,0.00002051677,0.9745393,0.007464836,0.01332287,0.004023509,0.00002255431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001190025,0.00007149151,0.9982907,0.00006396259,0.00001496965,0.00001210505,0.00003783681,0.0001011404,0.0002177693],"genre_scores_gemma":[0.05794293,0.0006015791,0.938423,0.0001158599,0.0001183997,0.0001583665,0.0004107212,0.00009223895,0.00213706],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003732639,"threshold_uncertainty_score":0.007421851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08784135663455689,"score_gpt":0.3622285018734854,"score_spread":0.2743871452389285,"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."}}