{"id":"W2290949015","doi":"10.3978/j.issn.2223-4292.2016.02.06","title":"Lung nodule segmentation in chest computed tomography using a novel background estimation method.","year":2016,"lang":"en","type":"article","venue":"PubMed","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Health Sciences; McMaster University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; National Cancer Institute; Foundation for the National Institutes of Health","keywords":"Segmentation; Nodule (geology); Lung cancer; Computer science; Task (project management); Artificial intelligence; Lung; Computed tomography; Radiology; Estimation; Cancer detection; Medicine; Pattern recognition (psychology); Cancer; Pathology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007495082,0.000954081,0.0007541024,0.002021723,0.0003541911,0.001254399,0.0007932281,0.001313878,0.001015251],"category_scores_gemma":[0.00188731,0.0004635783,0.0008228739,0.0008845853,0.0003356388,0.000765854,0.0007430721,0.0006721008,0.0009125546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003971046,"about_ca_system_score_gemma":0.0006196689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003556498,"about_ca_topic_score_gemma":0.004932969,"domain_scores_codex":[0.9995554,0.0000815509,0.00003009271,0.0001356335,0.0001494653,0.00004782773],"domain_scores_gemma":[0.9994107,0.0002393096,0.00007064734,0.0000462582,0.0001856874,0.00004724961],"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":[0.0005044058,0.0001993351,0.005500302,0.0004605992,0.0001802265,0.000561854,0.0001555915,0.03940808,0.2149117,0.001251261,0.003097608,0.733769],"study_design_scores_gemma":[0.00003880628,0.000120785,0.005426752,0.00003779413,0.0001293423,0.001194031,0.00004751299,0.9345025,0.05357861,0.0008536868,0.004028186,0.00004182854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03014618,0.00140287,0.9647738,0.0001850948,0.00008451595,0.0001123141,0.0001443798,0.002275222,0.0008755672],"genre_scores_gemma":[0.2275692,0.001326216,0.7672413,0.0002291899,0.0001501933,0.00009492342,0.0007880994,0.000399064,0.0022018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003556498,"threshold_uncertainty_score":0.007071555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05034857323068094,"score_gpt":0.3257495869012482,"score_spread":0.2754010136705672,"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."}}