{"id":"W1898227994","doi":"10.1109/crv.2015.25","title":"Lung Nodule Classification Using Deep Features in CT Images","year":2015,"lang":"en","type":"article","venue":"","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":377,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Cancer Institute; Foundation for the National Institutes of Health","keywords":"Lung cancer; CAD; Nodule (geology); Lung; Radiology; Computer-aided diagnosis; Artificial intelligence; Medicine; Cancer; Computer science; Lung cancer screening; Second opinion; Encoder; Pattern recognition (psychology); Medical physics; Pathology; Internal medicine; Engineering drawing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002909034,0.0004898382,0.0003333859,0.001264606,0.0001535722,0.0005023465,0.0003953901,0.0006558503,0.0007497818],"category_scores_gemma":[0.001317461,0.0001475799,0.0003896115,0.0005882266,0.0001789839,0.0003921328,0.0004011178,0.0002943346,0.0003105963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003618537,"about_ca_system_score_gemma":0.0002789502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007102247,"about_ca_topic_score_gemma":0.01074694,"domain_scores_codex":[0.9997713,0.00004039036,0.0000215392,0.00005115163,0.0000574233,0.00005816406],"domain_scores_gemma":[0.9996463,0.0001353946,0.00005693123,0.00004359823,0.00008727879,0.00003047062],"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":[0.0008098682,0.0005263629,0.09055115,0.0001963569,0.0001710716,0.001259522,0.0001136549,0.1222895,0.09661184,0.0007227217,0.00592697,0.6808211],"study_design_scores_gemma":[0.00001841694,0.0001517575,0.04082174,0.00002331228,0.00004556116,0.00052076,0.0000547667,0.9339651,0.02243749,0.001042876,0.0008988863,0.00001938195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9408756,0.0008471441,0.05367321,0.0003461202,0.00004397967,0.00008574991,0.001329021,0.001352939,0.001446183],"genre_scores_gemma":[0.9787839,0.0001529965,0.01867984,0.00006396306,0.00002029953,0.00001844617,0.001554051,0.00001261218,0.0007138043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007102247,"threshold_uncertainty_score":0.01412183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04352380629363663,"score_gpt":0.347446596619083,"score_spread":0.3039227903254464,"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."}}