{"id":"W2999759831","doi":"10.3390/ai1010003","title":"Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans","year":2020,"lang":"en","type":"article","venue":"AI","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":194,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Government of Canada; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"Deep learning; Nodule (geology); Lung cancer; Artificial intelligence; CAD; Computer science; Computed tomography; Lung cancer screening; Computer-aided diagnosis; Radiology; Cancer detection; Lung; Pattern recognition (psychology); Machine learning; Cancer; Medicine; Pathology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002521938,0.0000422093,0.00007872561,0.00001920566,0.00003071309,0.000007878815,0.000008930216,0.00001464819,0.00001481249],"category_scores_gemma":[0.00002647935,0.00003612053,0.0000153336,0.00006588855,0.000008033452,0.00003274067,0.000004188771,0.00005215115,0.000001216347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001178892,"about_ca_system_score_gemma":0.00001927326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001976371,"about_ca_topic_score_gemma":0.0006151221,"domain_scores_codex":[0.9996934,0.000007607421,0.00006481397,0.0001258199,0.00004108944,0.0000672963],"domain_scores_gemma":[0.9998624,0.00002313948,0.00002102542,0.00003327132,0.00002003371,0.00004013012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009577274,0.00003837841,0.8272921,0.0001958978,0.00004806108,0.000004119653,0.0006205874,0.0003439198,0.002469713,0.00007061606,0.0003289194,0.1684919],"study_design_scores_gemma":[0.002051938,0.000254137,0.7638612,0.0001191874,0.0001415892,0.000003130663,0.0001628797,0.2202342,0.004567733,0.00001349571,0.008519273,0.00007121453],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782468,0.002418603,0.003879746,0.01464615,0.00007409765,0.0004877952,0.000002089717,0.00003517727,0.0002095088],"genre_scores_gemma":[0.9982761,0.0005363706,0.00009763392,0.0007224808,0.00009841909,0.0002193635,0.000007640235,0.000007896224,0.0000340654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2198903,"threshold_uncertainty_score":0.1472952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02209730853621011,"score_gpt":0.3169243455282678,"score_spread":0.2948270369920576,"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."}}