{"id":"W4411866856","doi":"10.1109/trpms.2025.3584031","title":"Classification-Based Deep Learning Models for Lung Cancer and Disease Using Medical Images","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Radiation and Plasma Medical Sciences","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Specific Research Project of Guangxi for Research Bases and Talents; National Natural Science Foundation of China","keywords":"Lung cancer; Deep learning; Artificial intelligence; Disease; Computer science; Medicine; Machine learning; Pathology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005822384,0.00112288,0.0005619914,0.001073564,0.0001923854,0.0006024187,0.001454546,0.0009616782,0.001579744],"category_scores_gemma":[0.001090031,0.0003508978,0.001061558,0.0007769468,0.0003038145,0.0008392496,0.0005984069,0.001287247,0.0008436503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001174793,"about_ca_system_score_gemma":0.0009188175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01579669,"about_ca_topic_score_gemma":0.02103633,"domain_scores_codex":[0.9998121,0.00002973206,0.00001187481,0.00007108468,0.00004023626,0.00003496907],"domain_scores_gemma":[0.9997397,0.00008864891,0.00004328891,0.00002941654,0.00007984572,0.00001913799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002764995,0.000321166,0.009604683,0.0001869286,0.0001947363,0.0002079305,0.0000604683,0.6828942,0.007635134,0.004395362,0.008778575,0.2854442],"study_design_scores_gemma":[0.000004414886,0.00001803745,0.0005142369,0.000008355385,0.0000120788,0.00002376531,0.000003497709,0.9969357,0.0008405101,0.001165999,0.0004690676,0.000004296204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1547215,0.005065877,0.8226411,0.002428519,0.0003752824,0.0002656323,0.003532518,0.00487124,0.006098343],"genre_scores_gemma":[0.8723238,0.001938716,0.1111558,0.0007870389,0.0001711948,0.0002869458,0.004673195,0.0001098038,0.008553364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01579669,"threshold_uncertainty_score":0.0314095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02899418609351816,"score_gpt":0.3480308328489691,"score_spread":0.319036646755451,"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."}}