{"id":"W4408062444","doi":"10.18280/ts.420137","title":"Advanced Lung Cancer Segmentation Using Bilateral UNet and Hybrid Classification Using CT Images","year":2025,"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":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Medicine; Lung cancer; Computer science; Radiology; Artificial intelligence; Lung; Pattern recognition (psychology); Oncology; Internal medicine","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":[0.0006795048,0.0006120378,0.0006462307,0.001796425,0.0003002766,0.001287382,0.0005053976,0.001245732,0.001723076],"category_scores_gemma":[0.001193366,0.0003531444,0.0008866451,0.001052197,0.000259696,0.0008607899,0.000576058,0.0004240885,0.0006732736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002646935,"about_ca_system_score_gemma":0.0005469733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003040183,"about_ca_topic_score_gemma":0.005710446,"domain_scores_codex":[0.9997334,0.00003974829,0.00002160191,0.00006777421,0.00009081171,0.0000466567],"domain_scores_gemma":[0.9996338,0.00009476714,0.00003684292,0.00005850099,0.0001442023,0.00003192124],"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":[0.0007934654,0.0001456089,0.01757972,0.0003795334,0.0002120792,0.0009000607,0.0001827864,0.03545374,0.3101772,0.001911174,0.002434439,0.6298301],"study_design_scores_gemma":[0.00003795466,0.0002222362,0.02759725,0.00005334151,0.0002646403,0.002787223,0.0001304211,0.8710384,0.08991684,0.00236619,0.005511491,0.00007394436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.180842,0.001600666,0.8112845,0.0003639147,0.00009691938,0.000140118,0.0005581773,0.00206411,0.003049574],"genre_scores_gemma":[0.6026217,0.000871159,0.3905708,0.0001691097,0.0001263916,0.00009381319,0.000746386,0.0003042976,0.004496193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003040183,"threshold_uncertainty_score":0.006044984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01789817696824245,"score_gpt":0.3460526444306708,"score_spread":0.3281544674624283,"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."}}