{"id":"W4323035249","doi":"10.1002/9781119790686.ch34","title":"AI for Medical Image Processing","year":2023,"lang":"en","type":"other","venue":"AI in Clinical Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Convolutional neural network; Computer science; Scope (computer science); Quality assurance; Medical imaging; Image quality; Image processing; Medical physics; Artificial intelligence; Artificial neural network; Quality (philosophy); Image (mathematics); Computer vision; Medicine; Pathology","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":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004825739,0.000460519,0.002020454,0.0005844292,0.00004793135,0.00001805686,0.0004115968,0.001205227,0.002646131],"category_scores_gemma":[0.03188475,0.0003324741,0.0003068347,0.0005069746,0.001160176,0.00004248699,0.0001204487,0.003348644,0.0001945741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007855718,"about_ca_system_score_gemma":0.0006510703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003844871,"about_ca_topic_score_gemma":0.0002662294,"domain_scores_codex":[0.9951748,0.0001497639,0.001842034,0.0009529891,0.001192027,0.0006884261],"domain_scores_gemma":[0.9958016,0.002165581,0.0004217366,0.000605995,0.0001345559,0.0008705389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009695561,0.0001709172,0.003473656,0.0008853035,0.00008820775,0.0005214867,0.00003586859,1.639058e-7,0.000005217174,0.0001252083,0.787071,0.207526],"study_design_scores_gemma":[0.00847913,0.0007431832,0.002951664,0.0149977,0.0003139942,0.0000808977,0.00003855811,0.009923042,9.29173e-7,0.0008450078,0.9612839,0.0003419749],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.0001102806,0.005485483,0.02259967,0.6661513,0.008632717,0.003246804,0.00002648423,0.002657938,0.2910894],"genre_scores_gemma":[0.002403908,0.008784832,0.009885133,0.20628,0.03576291,0.0004391207,0.0003929774,0.005267673,0.7307834],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4598712,"threshold_uncertainty_score":0.9999127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04139267117788451,"score_gpt":0.4816122287065652,"score_spread":0.4402195575286807,"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."}}