{"id":"W2296030939","doi":"10.1016/j.mri.2016.03.006","title":"Correlation between subjective and objective assessment of magnetic resonance (MR) images","year":2016,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Biogen Idec; Genentech; National Institutes of Health; Servier; Janssen Research and Development; Universiti Malaya; Elan; Alzheimer's Disease Neuroimaging Initiative; GE Healthcare; Pfizer; BioClinica; Meso Scale Diagnostics; Johnson and Johnson; Takeda Pharmaceutical Company; Medpace; Eli Lilly and Company; Bristol-Myers Squibb; Novartis Pharmaceuticals Corporation; F. Hoffmann-La Roche; Merck; Alzheimer's Drug Discovery Foundation; Synarc; Fujirebio Europe; Alzheimer's Association","keywords":"Correlation; Gaussian blur; Spearman's rank correlation coefficient; Mean opinion score; JPEG; JPEG 2000; Image quality; Artificial intelligence; Rank correlation; Correlation coefficient; Mathematics; Distortion (music); Magnetic resonance imaging; Image processing; Computer science; Pattern recognition (psychology); Image compression; Statistics; Data compression; Medicine; Radiology; Image (mathematics); Metric (unit)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000776999,0.0003125117,0.0004370564,0.000210434,0.0001758769,0.0001659298,0.0005314667,0.00006328266,0.00003875159],"category_scores_gemma":[0.0001672558,0.0002521831,0.00008644179,0.0004924909,0.0003806091,0.001140948,0.0004469299,0.0002036213,0.00001462773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001595658,"about_ca_system_score_gemma":0.0001903916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001571873,"about_ca_topic_score_gemma":0.000006228654,"domain_scores_codex":[0.9970343,0.0003354559,0.0006116175,0.000851737,0.0006195061,0.0005473761],"domain_scores_gemma":[0.9977943,0.0007547589,0.0002631364,0.0007716239,0.0002947827,0.0001213835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000008850416,0.00004378024,0.2888184,0.00002068535,0.000003394945,0.00002311086,0.000503268,0.000001136178,0.003713045,0.006397444,0.0002253979,0.7002415],"study_design_scores_gemma":[0.001152213,0.0003165898,0.9786695,0.000291534,0.00002267718,0.00002428809,0.0001350011,0.005043611,0.003586263,0.006285059,0.004110122,0.0003631707],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06849761,0.03485545,0.8847862,0.003277977,0.0003667365,0.0008572939,0.0000659742,0.0002123598,0.007080361],"genre_scores_gemma":[0.9672526,0.0003927588,0.03082318,0.0001670783,0.0000907259,0.00006164415,0.000001953193,0.00002507353,0.001184999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.898755,"threshold_uncertainty_score":0.999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01285431853469814,"score_gpt":0.2892828154500066,"score_spread":0.2764284969153085,"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."}}