{"id":"W2150903265","doi":"10.1016/j.media.2013.05.008","title":"Medical image processing on the GPU – Past, present and future","year":2013,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":405,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Vetenskapsrådet","keywords":"Computer science; Image processing; Graphics processing unit; Medical imaging; Artificial intelligence; Computer vision; Histogram; General-purpose computing on graphics processing units; Interpolation (computer graphics); Graphics; Computer graphics; Image registration; Computer graphics (images); Image (mathematics); Parallel computing","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":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001766116,0.0006675932,0.002761077,0.0004668447,0.0002756138,0.0001971141,0.0008903358,0.0008700231,0.0138912],"category_scores_gemma":[0.0009361858,0.0003299824,0.001138851,0.002030048,0.001174915,0.00009530483,0.0004246667,0.00255981,0.0003035779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008595035,"about_ca_system_score_gemma":0.0006669224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006614589,"about_ca_topic_score_gemma":0.000002522216,"domain_scores_codex":[0.9937102,0.0003877532,0.001217419,0.0009950493,0.003074428,0.0006151573],"domain_scores_gemma":[0.9956487,0.0007579516,0.0004300557,0.001300755,0.0002428064,0.001619744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002212818,0.0001943012,0.000003894505,0.002731241,0.0008384433,0.0002107743,0.00002080366,1.830255e-9,7.33156e-7,0.00009593969,0.1944552,0.8014465],"study_design_scores_gemma":[0.0002016631,0.00004774392,0.00001044442,0.004995549,0.01056416,0.0001975089,0.000041442,0.002495453,0.000002452165,0.00005855529,0.9810753,0.0003097476],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000006002646,0.8201616,0.007655297,0.1667378,0.00005644267,0.001362471,0.00002220953,0.0002580944,0.003740065],"genre_scores_gemma":[0.000007772175,0.9832816,0.004707167,0.003833929,0.005459144,0.0009496653,0.0003634175,0.00008386511,0.001313506],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8011367,"threshold_uncertainty_score":0.9999152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03209868690587763,"score_gpt":0.3864913773342081,"score_spread":0.3543926904283305,"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."}}