{"id":"W2402138580","doi":"10.1615/critrevbiomedeng.2015011026","title":"Review of Texture Quantification of CT Images for Classification of Lung Diseases","year":2015,"lang":"en","type":"article","venue":"Critical Reviews in Biomedical Engineering","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Joseph’s Healthcare Hamilton; St Joseph's Health Care; McMaster University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Texture (cosmology); Support vector machine; Segmentation; Computer science; Fractal analysis; Lung cancer; Lung; Fractal; Image texture; Image segmentation; Fractal dimension; Medicine; Pathology; Mathematics; Image (mathematics); Internal medicine","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"],"consensus_categories":[],"category_scores_codex":[0.001444091,0.0001224127,0.0008289966,0.0001545273,0.000007087708,0.000002465318,0.000131322,0.00005212538,0.00001873767],"category_scores_gemma":[0.03030042,0.00009466278,0.000173406,0.0004055149,0.0002563808,0.00005011339,0.00002538776,0.0002054858,0.000001058274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004421157,"about_ca_system_score_gemma":0.00009979783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004729802,"about_ca_topic_score_gemma":3.245486e-8,"domain_scores_codex":[0.9982083,0.00005807554,0.001052636,0.0001926925,0.0003128534,0.0001754324],"domain_scores_gemma":[0.9985412,0.00053502,0.0001578628,0.0002776312,0.0002242718,0.0002640089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001254414,0.001493846,0.005386373,0.4772421,0.00009431611,0.00001863488,0.0001284659,0.00003073315,0.1105972,0.020437,0.04477084,0.339675],"study_design_scores_gemma":[0.003200466,0.0006437483,0.01419382,0.2293202,0.001127969,0.00008642238,0.00007718711,0.2933477,0.003513194,0.0006867185,0.4532673,0.0005352079],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.00226695,0.6290561,0.3584951,0.007846761,0.0005453029,0.001507265,0.00006087147,0.00004018921,0.0001814794],"genre_scores_gemma":[0.8647789,0.1044758,0.02936986,0.0005592093,0.0003071297,0.0001872002,0.0002497194,0.00004950304,0.00002263455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.862512,"threshold_uncertainty_score":0.9778678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03988242801352217,"score_gpt":0.3865376620677046,"score_spread":0.3466552340541825,"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."}}