{"id":"W4388240489","doi":"10.1109/icjece.2023.3289609","title":"TransAttU-Net Deep Neural Network for Brain Tumor Segmentation in Magnetic Resonance Imaging","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Preprocessor; Artificial neural network; Magnetic resonance imaging; Deep learning; Pattern recognition (psychology); Image segmentation; Pixel; Brain tumor; Computer vision; Medicine; Radiology","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":[0.0005903058,0.001033247,0.0006744784,0.0007688712,0.0004093926,0.0006947877,0.001307783,0.001240884,0.002802043],"category_scores_gemma":[0.001354217,0.0003955345,0.0006965877,0.000944341,0.0003713343,0.0009892546,0.0008786002,0.001349964,0.001012036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001261561,"about_ca_system_score_gemma":0.001499965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02164187,"about_ca_topic_score_gemma":0.03092583,"domain_scores_codex":[0.9997442,0.00004392504,0.00001507443,0.00007526799,0.00007023118,0.00005136494],"domain_scores_gemma":[0.9997856,0.00006054817,0.00002404662,0.00002420187,0.00008574925,0.00001986104],"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":[0.0006112625,0.000350888,0.0018926,0.0002286957,0.0001668051,0.0002089515,0.00008751553,0.3703129,0.01155829,0.005871303,0.0311916,0.5775192],"study_design_scores_gemma":[0.00001165469,0.0000488316,0.0002079649,0.00001232944,0.00001434413,0.00003586822,0.00001197537,0.9928889,0.003040989,0.002183501,0.001535805,0.000007780436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1549168,0.008421509,0.8002822,0.002255721,0.0005687908,0.0002958296,0.002622737,0.01626481,0.01437153],"genre_scores_gemma":[0.6746329,0.002085292,0.2914641,0.001088319,0.0001488055,0.0002985298,0.007445985,0.0004884433,0.02234764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02164187,"threshold_uncertainty_score":0.04303175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01160097222929699,"score_gpt":0.2054163804651309,"score_spread":0.1938154082358339,"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."}}