{"id":"W3204704328","doi":"10.18280/ts.380421","title":"Lung Cancer Classification Using Squeeze and Excitation Convolutional Neural Networks with Grad Cam++ Class Activation Function","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lung cancer; Lung; Nodule (geology); Robustness (evolution); Convolutional neural network; Treatment of lung cancer; Medicine; Radiology; Solitary pulmonary nodule; Artificial intelligence; Computer science; Pattern recognition (psychology); Pathology; Internal medicine; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008019306,0.0001513355,0.0001681759,0.00005947372,0.000156363,0.00004585302,0.00001729504,0.00006485069,0.0002076609],"category_scores_gemma":[0.000005022623,0.0001271447,0.000042067,0.0001926937,0.00004790517,0.0002096118,0.00000931339,0.00010909,5.471277e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005844229,"about_ca_system_score_gemma":0.000165419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001259982,"about_ca_topic_score_gemma":0.00008853237,"domain_scores_codex":[0.9989385,0.00005177948,0.0002107976,0.0003205507,0.0003000405,0.0001783457],"domain_scores_gemma":[0.9994066,0.00005251367,0.0001219809,0.00009574165,0.0002327718,0.00009043198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002512348,0.001068161,0.8695538,0.0004326033,0.001238005,0.00003908658,0.0006037815,0.05938599,0.03413385,0.005211278,0.002554634,0.02326643],"study_design_scores_gemma":[0.002750382,0.000222276,0.5580831,0.0001863924,0.0004697409,0.00002294143,0.0001386073,0.4370411,0.0006156274,0.00002448661,0.0003270382,0.0001183594],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9567879,0.0007908875,0.03881934,0.002827226,0.0001482565,0.000482146,0.00001062996,0.00004169767,0.0000919524],"genre_scores_gemma":[0.9978714,0.0001165105,0.0004411105,0.0005889426,0.0004223117,0.0001665271,0.0003223214,0.00001905887,0.00005183866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3776551,"threshold_uncertainty_score":0.5184812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961284207956446,"score_gpt":0.2895119010154805,"score_spread":0.2598990589359161,"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."}}