{"id":"W4384570451","doi":"10.1007/978-3-031-33842-7_17","title":"Multi-modal Brain Tumour Segmentation Using Transformer with Optimal Patch Size","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Transformer; Modal; Segmentation; Speech recognition; Artificial intelligence; Electrical engineering; Voltage; Materials science; Composite material","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004521951,0.0004046771,0.0003026075,0.0004212394,0.000373942,0.0002836609,0.0006324129,0.000195206,0.00004142647],"category_scores_gemma":[0.0002161737,0.0003505694,0.00008545335,0.0007221505,0.0007079696,0.0003951867,0.00005793055,0.0006531415,0.00005245371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003022934,"about_ca_system_score_gemma":0.0003390361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003334453,"about_ca_topic_score_gemma":0.0001365864,"domain_scores_codex":[0.9969057,0.00005672916,0.0003823092,0.001281516,0.0008832101,0.0004905317],"domain_scores_gemma":[0.9984482,0.0007083521,0.0002305943,0.00039154,0.00009352988,0.0001278129],"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":[0.00006482765,0.00005036042,0.00004843686,0.00005243482,0.000005669293,0.0001081348,0.001313535,0.0615988,0.8231286,0.000464166,0.000008739573,0.1131563],"study_design_scores_gemma":[0.0009268576,0.0002749447,0.0006843365,0.000279218,0.00001734265,0.0002349156,0.000003979645,0.6727868,0.321886,0.001997736,0.0001286512,0.0007792458],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01207833,0.00000807045,0.9848459,0.0009634585,0.0008706563,0.0005856361,0.00001678475,0.0002168705,0.0004142917],"genre_scores_gemma":[0.8696904,0.00001515147,0.1255196,0.002618164,0.000314371,0.00002511369,0.00000596095,0.000110918,0.001700342],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8593263,"threshold_uncertainty_score":0.9998946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04753385199735046,"score_gpt":0.2847275195661993,"score_spread":0.2371936675688488,"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."}}