{"id":"W4318833378","doi":"10.1002/ima.22853","title":"Effective data augmentation for brain tumor segmentation","year":2023,"lang":"en","type":"article","venue":"International Journal of Imaging Systems and Technology","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Outlier; Segmentation; Dice; Robustness (evolution); Training set; Artificial intelligence; Generalization; Pattern recognition (psychology); Data set; Synthetic data; Sampling (signal processing); Machine learning; Mathematics; Computer vision; Statistics","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":[0.0005384422,0.00006586425,0.000103272,0.0006146904,0.00007441713,0.00009948193,0.00035645,0.00002521593,0.00000278065],"category_scores_gemma":[0.00108839,0.00005954283,0.00002146246,0.0002847327,0.00007187032,0.0003646284,0.00007388415,0.00009523019,0.00000815222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006418469,"about_ca_system_score_gemma":0.00002766776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007262549,"about_ca_topic_score_gemma":0.000001457669,"domain_scores_codex":[0.9991351,0.00005734502,0.0003046994,0.0001898965,0.0002180057,0.00009493301],"domain_scores_gemma":[0.9989134,0.0003250706,0.0003912855,0.0001221288,0.000223725,0.00002440812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007089428,0.0000316742,0.001775724,0.00002572805,0.00003468449,0.00005254171,0.000131786,0.00004287151,0.8658643,0.01225809,0.006590725,0.113121],"study_design_scores_gemma":[0.008598339,0.0005690466,0.008308112,0.0004951842,0.00008661409,0.008731216,0.008667327,0.2461719,0.5960576,0.01908516,0.1026587,0.0005708019],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8473332,0.0002735868,0.09162439,0.0516908,0.00726017,0.00107657,0.0001669738,0.0003584048,0.0002158956],"genre_scores_gemma":[0.9990933,0.00002392569,0.0002556172,0.000244459,0.0001844634,0.00003569327,0.00001450512,0.00001006728,0.0001380018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2698067,"threshold_uncertainty_score":0.2428086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03430739277089406,"score_gpt":0.3422438189296304,"score_spread":0.3079364261587363,"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."}}