{"id":"W4396559668","doi":"10.18280/ts.410243","title":"Classification of Lung Adenocarcinoma Using Convolutional Neural Networks: A Bioinformatics Approach","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Adenocarcinoma; Artificial intelligence; Computational biology; Bioinformatics; Machine learning; Biology; Medicine; Internal medicine; Cancer","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007760515,0.0005841737,0.0005006746,0.001982803,0.0003832989,0.000865898,0.0005822681,0.000554207,0.0006030578],"category_scores_gemma":[0.001738969,0.0002146308,0.0006813182,0.001333374,0.0002444579,0.0004611474,0.0005758469,0.000631622,0.0003677231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001107373,"about_ca_system_score_gemma":0.001275641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00912644,"about_ca_topic_score_gemma":0.01434034,"domain_scores_codex":[0.9996334,0.00007319544,0.00002968887,0.0001083113,0.00009870811,0.0000567218],"domain_scores_gemma":[0.9995925,0.000140469,0.0000606543,0.00004052384,0.0001389757,0.0000269811],"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":[0.0005004863,0.0005051926,0.05736255,0.0004184077,0.0003265195,0.0007778684,0.0001980033,0.2609117,0.08875144,0.00801489,0.00829163,0.5739413],"study_design_scores_gemma":[0.00001132,0.00008407737,0.008056469,0.00002158598,0.00004586294,0.0001217748,0.00006047832,0.9743683,0.01068376,0.004222639,0.002308333,0.00001533132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3150139,0.002173136,0.6680772,0.002217751,0.0001283344,0.0003745682,0.004544018,0.003495344,0.003975844],"genre_scores_gemma":[0.7268363,0.001057707,0.2608591,0.0004002592,0.00008763756,0.0003143757,0.007591687,0.00008075838,0.002772241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00912644,"threshold_uncertainty_score":0.01814663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834618412363138,"score_gpt":0.289869088207222,"score_spread":0.2615229040835906,"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."}}