{"id":"W4392199409","doi":"10.18280/mmep.110223","title":"Medical Image Segmentation Using Enhanced Residual U-Net Architecture","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Residual; Segmentation; Net (polyhedron); Architecture; Image (mathematics); Artificial intelligence; Image segmentation; Computer science; Computer vision; Pattern recognition (psychology); Mathematics; Algorithm; Geography; Geometry","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":[0.0002624135,0.0001289648,0.0001188034,0.0000974397,0.0000732727,0.0001618499,0.00007513484,0.00007984238,0.00006533549],"category_scores_gemma":[0.0001539847,0.0001090742,0.00003143964,0.0001803155,0.00004742439,0.0001076285,0.00002454548,0.0002744405,0.00003007536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002636342,"about_ca_system_score_gemma":0.00001890618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001810284,"about_ca_topic_score_gemma":1.425672e-7,"domain_scores_codex":[0.9989356,0.00002626634,0.0002286792,0.00030299,0.0003102841,0.0001961764],"domain_scores_gemma":[0.9995288,0.0002217431,0.00001934249,0.000108154,0.000009888078,0.0001120911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003785298,0.00002294207,9.208311e-8,0.0007470463,0.000005655804,0.000009436098,0.0009532019,0.3264564,0.6540171,0.01424334,0.00001213478,0.003528874],"study_design_scores_gemma":[0.00007622536,0.00001634478,3.930182e-7,0.0003089189,0.000009330532,0.00009452981,0.00001580372,0.9091468,0.07879731,0.01129385,0.0001236439,0.0001168125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1350944,0.00007986241,0.8635769,0.0003303534,0.0001184095,0.0001248535,0.000002013454,0.0003428292,0.0003303723],"genre_scores_gemma":[0.971508,0.00004112469,0.02810328,0.00004678202,0.0001020337,0.00002737137,0.000001628922,0.00003294981,0.0001368398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8364136,"threshold_uncertainty_score":0.4447917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03599841229690045,"score_gpt":0.2621879352210268,"score_spread":0.2261895229241263,"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."}}