{"id":"W2741247953","doi":"10.1016/j.media.2017.07.006","title":"Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"FP7 Ideas: European Research Council; Israel Science Foundation; United States-Israel Binational Science Foundation","keywords":"Computer science; Artificial intelligence; Voxel; Pattern recognition (psychology); Dynamic contrast-enhanced MRI; Artificial neural network; Diffusion MRI; Classifier (UML); Noise reduction; Noise (video); Deep learning; Contrast (vision); Magnetic resonance imaging; Image (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":[0.001681698,0.0001802051,0.0007666936,0.0002121466,0.0002524302,0.0002238773,0.001648086,0.0001306046,0.00008190193],"category_scores_gemma":[0.001385535,0.0001485895,0.000482782,0.0004630983,0.0004427931,0.0007060051,0.0002315406,0.0001534707,0.000001049862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001902462,"about_ca_system_score_gemma":0.00007744048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006758699,"about_ca_topic_score_gemma":0.0002820232,"domain_scores_codex":[0.9975695,0.0002377364,0.0006631128,0.0004383358,0.0007188896,0.000372422],"domain_scores_gemma":[0.9972308,0.0005521003,0.0006247975,0.0009614101,0.0004362064,0.0001947243],"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":[0.0003195383,0.0004610189,0.007281371,0.0001632093,0.002466349,0.0002569234,0.003453789,0.00727082,0.09074388,0.001130811,0.0008171774,0.8856351],"study_design_scores_gemma":[0.0006915873,0.00009991779,0.001026757,0.00003273559,0.0002547184,0.000003540256,0.00003945118,0.9166769,0.08021171,0.0007551074,0.00004358059,0.00016394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02789131,0.0004279969,0.9702504,0.000835515,0.0002014503,0.0001367367,0.000002296476,0.00003033232,0.0002239247],"genre_scores_gemma":[0.8233924,0.00003231265,0.1761504,0.0002159311,0.0001261258,0.00001261029,0.00001033858,0.000008163868,0.00005170427],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9094061,"threshold_uncertainty_score":0.6059304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02335959899050011,"score_gpt":0.3287790501383719,"score_spread":0.3054194511478718,"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."}}