{"id":"W4388100550","doi":"10.18280/ts.400510","title":"Advances in Brain Tumor Segmentation and Skull Stripping: A 3D Residual Attention U-Net Approach","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Residual; Skull; Segmentation; Stripping (fiber); Net (polyhedron); Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Mathematics; Geology; Algorithm; Materials science; Paleontology; 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":[0.0004691146,0.0001256405,0.0001118761,0.0002578661,0.0001417564,0.00008120004,0.00009545411,0.00003146895,0.00008354966],"category_scores_gemma":[0.0000948363,0.0001269309,0.00002567615,0.0006401315,0.00007446077,0.0005084236,0.00002788455,0.0001187983,0.00003856749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005888071,"about_ca_system_score_gemma":0.00002006559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006930161,"about_ca_topic_score_gemma":0.00001589735,"domain_scores_codex":[0.998485,0.0002069137,0.0002944162,0.0004385181,0.0003378889,0.0002372926],"domain_scores_gemma":[0.9995744,0.0001419342,0.0001154888,0.0000940111,0.00001540281,0.00005875033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001356229,0.000197049,0.001889148,0.00009941086,0.00000333873,0.00001983708,0.0009861595,0.0008820058,0.9266863,0.002858512,0.001095926,0.06514665],"study_design_scores_gemma":[0.01225903,0.00130773,0.3683887,0.000238596,0.00005738427,0.0002009722,0.01138116,0.3438561,0.2242447,0.004684319,0.03171759,0.001663818],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880632,0.00004151716,0.007337323,0.001438307,0.0001923573,0.0006782305,0.00002090838,0.0002568455,0.001971316],"genre_scores_gemma":[0.9984409,0.00006841805,0.0003244596,0.0004834513,0.00008178126,0.0001395381,0.0000405128,0.00001430044,0.0004066537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7024417,"threshold_uncertainty_score":0.5176092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03830068067831319,"score_gpt":0.2792042448360025,"score_spread":0.2409035641576893,"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."}}