{"id":"W3134090113","doi":"10.1016/j.compmedimag.2021.101897","title":"Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging","year":2021,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Artifact (error); Computer vision; Deep learning; Image quality; Motion (physics); Pattern recognition (psychology); Transfer of learning; Neuroimaging; Artificial neural network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00223621,0.001115296,0.0007530528,0.001556672,0.0002519959,0.001152829,0.0009300059,0.0009529328,0.001554085],"category_scores_gemma":[0.005701451,0.0003364845,0.0006582845,0.0006604031,0.0003146884,0.0008002769,0.001009069,0.0007450819,0.0005073579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005621759,"about_ca_system_score_gemma":0.0007538175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005416679,"about_ca_topic_score_gemma":0.00954198,"domain_scores_codex":[0.9991768,0.0001306278,0.00007285467,0.000168098,0.0003546247,0.00009695478],"domain_scores_gemma":[0.9980302,0.0004287967,0.000300119,0.0002477263,0.0008872059,0.000105836],"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":[0.001444394,0.0002620857,0.01526935,0.0006720416,0.0006945264,0.0004258054,0.0001385261,0.04591475,0.1369344,0.001593611,0.006912303,0.7897383],"study_design_scores_gemma":[0.00007297673,0.0005574093,0.02322787,0.0001095192,0.0004590302,0.001292262,0.00006859446,0.8550328,0.1141633,0.001605422,0.003339349,0.00007154702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2043484,0.003105923,0.7850732,0.0003324315,0.0002492536,0.0003230381,0.001178912,0.00300174,0.00238715],"genre_scores_gemma":[0.7344582,0.001448128,0.2570809,0.0002551957,0.0001420316,0.0001161656,0.001807791,0.0007597228,0.003931888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005416679,"threshold_uncertainty_score":0.01182634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08627831177713087,"score_gpt":0.4421554781936364,"score_spread":0.3558771664165055,"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."}}