{"id":"W2910002198","doi":"10.1016/j.media.2019.01.005","title":"Recurrent inference machines for reconstructing heterogeneous MRI data","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":85,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Artificial intelligence; Computer science; Inference; Overfitting; Deep learning; Iterative reconstruction; Process (computing); Imaging phantom; SIGNAL (programming language); Inverse problem; Compressed sensing; Pattern recognition (psychology); Benchmark (surveying); Machine learning; Real-time MRI; Magnetic resonance imaging; Artificial neural network; Mathematics; Radiology; Medicine","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":[0.002859333,0.001230244,0.001751958,0.001209069,0.0004871826,0.001217561,0.002059511,0.001986992,0.001879118],"category_scores_gemma":[0.01118497,0.001251386,0.001714793,0.001318551,0.0008375252,0.001497675,0.001467239,0.00282927,0.001039028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008888948,"about_ca_system_score_gemma":0.000909241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007392986,"about_ca_topic_score_gemma":0.01171045,"domain_scores_codex":[0.9991258,0.0003385486,0.0000689088,0.0002378548,0.0001510905,0.00007774685],"domain_scores_gemma":[0.9932497,0.005257578,0.0004285548,0.0005526782,0.0003837556,0.0001278976],"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":[0.0002401748,0.0001122823,0.001471671,0.00014256,0.0002580644,0.0002222129,0.000121632,0.7244864,0.003967252,0.01565476,0.004052111,0.2492708],"study_design_scores_gemma":[0.000003916653,0.000007706529,0.00005593575,0.000003906327,0.000009279939,0.000009254874,0.00000269353,0.9949209,0.00033408,0.004497226,0.0001513805,0.000003713964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008301833,0.0003990616,0.9898914,0.0001645307,0.00003747931,0.00002211625,0.0001314917,0.0008091892,0.0002429364],"genre_scores_gemma":[0.3917063,0.0008925736,0.5976992,0.0003541412,0.0003103456,0.0002834982,0.002176309,0.0005694201,0.006008189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007392986,"threshold_uncertainty_score":0.01512182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03739816782364099,"score_gpt":0.4081645798559363,"score_spread":0.3707664120322953,"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."}}