{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00209612,0.0001821243,0.0004102192,0.0002266235,0.0001182308,0.0000671512,0.0001251775,0.00007876703,0.0000525285],"category_scores_gemma":[0.001288757,0.0001765008,0.00006510525,0.0007498618,0.0001065432,0.0001486309,0.0002280858,0.0004303403,0.000002165546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006626453,"about_ca_system_score_gemma":0.000203649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006153312,"about_ca_topic_score_gemma":0.00001400498,"domain_scores_codex":[0.997651,0.0004732675,0.0005297113,0.0005170384,0.0005283331,0.0003006725],"domain_scores_gemma":[0.9984288,0.0002720137,0.0001159906,0.000498975,0.0003246843,0.0003595028],"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":[0.00005135707,0.0003334642,0.002515605,0.0002822102,0.00001718463,0.0002323925,0.000489457,0.0001281188,0.1014598,0.003313182,0.0001518943,0.8910254],"study_design_scores_gemma":[0.001808747,0.00002109554,0.02436207,0.0004749191,0.000073845,0.000221126,0.00009155634,0.9561312,0.001804096,0.01433078,0.0004936283,0.0001869704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2131664,0.0001812756,0.7776117,0.008334218,0.00005900083,0.0003999501,0.000003012679,0.0001516366,0.00009284239],"genre_scores_gemma":[0.2822073,0.0001207283,0.7130331,0.004397118,0.00008168015,0.00006767723,0.00006040198,0.00002579698,0.000006144068],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9560031,"threshold_uncertainty_score":0.7197494,"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."}}