{"id":"W4414655274","doi":"10.3390/bioengineering12101062","title":"Comparative Analysis of Foundational, Advanced, and Traditional Deep Learning Models for Hyperpolarized Gas MRI Lung Segmentation: Robust Performance in Data-Constrained Scenarios","year":2025,"lang":"en","type":"article","venue":"Bioengineering","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Lawson Health Research Institute; Western University","funders":"","keywords":"Deep learning; Segmentation; Medical imaging; Pyramid (geometry); Feature (linguistics); Big data","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001470375,0.0001000703,0.0002678815,0.000236056,0.00008166324,0.00002583519,0.0001228451,0.00001977741,0.00002379159],"category_scores_gemma":[0.000003883647,0.0001084135,0.00004601127,0.0004334394,0.00004596331,0.0003585884,0.00004673421,0.0001127358,2.45766e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005288583,"about_ca_system_score_gemma":0.0000936854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001509274,"about_ca_topic_score_gemma":0.00000548142,"domain_scores_codex":[0.9993045,0.00001517229,0.0002201239,0.0002095147,0.000102181,0.000148494],"domain_scores_gemma":[0.9995773,0.000167929,0.00005180618,0.0001165317,0.00005719547,0.00002928118],"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":[0.0000382262,0.0000390932,0.01420172,0.00004757163,0.000545345,8.637404e-8,0.0003670002,0.9601457,0.001509654,0.01745446,0.000007372545,0.005643839],"study_design_scores_gemma":[0.0007744291,0.000005416415,0.002035712,0.00003040198,0.00009935561,6.772715e-8,0.0008353522,0.9953877,0.0003228422,0.0003817994,0.00003648236,0.0000904527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2684279,0.000092208,0.7306585,0.00003524934,0.00002185979,0.0002027222,0.0001063882,0.000008730859,0.0004464347],"genre_scores_gemma":[0.9879055,0.00001439955,0.01092749,0.000003642935,0.00002348776,0.00004972816,0.00103397,0.000004926156,0.00003685866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.719731,"threshold_uncertainty_score":0.4420976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0580565668218483,"score_gpt":0.3041762577152908,"score_spread":0.2461196908934425,"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."}}