{"id":"W1968301261","doi":"10.1016/j.media.2013.08.006","title":"Denoising and fast diffusion imaging with physically constrained sparse dictionary learning","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Artificial intelligence; Computer science; Noise reduction; Dictionary learning; Diffusion MRI; Pattern recognition (psychology); Gaussian; Diffusion; Sparse approximation; Computer vision; Physics","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":[0.0009868573,0.0006327719,0.0008001698,0.0006943118,0.0002568977,0.0009728214,0.000789849,0.001447802,0.00168323],"category_scores_gemma":[0.004502047,0.0006122026,0.0006671866,0.0009490995,0.0009772754,0.0017141,0.001330124,0.001727038,0.0005331582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003198344,"about_ca_system_score_gemma":0.0006257395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001589674,"about_ca_topic_score_gemma":0.001832034,"domain_scores_codex":[0.9996597,0.000122901,0.00002161672,0.00006081367,0.0001160307,0.00001901711],"domain_scores_gemma":[0.998908,0.0006070759,0.0001070003,0.0001784062,0.0001627105,0.00003675793],"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":[0.0002949379,0.000101404,0.0007197151,0.0005844464,0.0001871494,0.0002298717,0.0002434371,0.3382022,0.04593343,0.2448675,0.007314032,0.3613219],"study_design_scores_gemma":[0.00001719806,0.00002965057,0.0001617904,0.00001858274,0.00001596162,0.0001542,0.00001861584,0.9424489,0.005731,0.04744014,0.003945515,0.00001851556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002775199,0.0003160163,0.9958892,0.000234601,0.00005185836,0.00001264967,0.0000321797,0.00006444523,0.0006238284],"genre_scores_gemma":[0.104354,0.001538999,0.8875389,0.0001934489,0.0001942414,0.00007975673,0.0002254191,0.0001331606,0.005742077],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00168323,"threshold_uncertainty_score":0.00563097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263309381635904,"score_gpt":0.29270872219528,"score_spread":0.280075628378921,"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."}}