{"id":"W3156970058","doi":"10.21013/jas.v16.n2.p2","title":"Automatic MRI Brain Tumor Segmentation Techniques: A Survey","year":2021,"lang":"en","type":"article","venue":"IRA-International Journal of Applied Sciences (ISSN 2455-4499)","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Segmentation; Brain tumor; Market segmentation; Image segmentation; Artificial intelligence; Computer science; Computer vision; Medicine; Pathology","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":[0.002300759,0.0002171359,0.0002840913,0.0004707628,0.0003208219,0.0005324198,0.001126677,0.00007022611,0.0006048782],"category_scores_gemma":[0.001197615,0.0001953603,0.0001545831,0.001122643,0.0003826681,0.0006347346,0.000114076,0.000353794,0.00008927623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002635001,"about_ca_system_score_gemma":0.0005157391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001370117,"about_ca_topic_score_gemma":0.00003511658,"domain_scores_codex":[0.9960982,0.0003261366,0.0009089691,0.0005228213,0.001829305,0.0003146271],"domain_scores_gemma":[0.9973173,0.0008065127,0.0009954488,0.00022399,0.0004845941,0.0001721866],"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":[0.0001238902,0.0003172503,0.000680389,0.00001557105,0.00003116225,0.0001682776,0.0007787663,0.0003050582,0.9373,0.00819522,0.004315032,0.04776933],"study_design_scores_gemma":[0.0007993202,0.0001642881,0.00647183,0.00008506613,0.00001539339,0.001046714,0.001095423,0.006002236,0.9744577,0.004096162,0.005459734,0.0003061721],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9410331,0.00005510914,0.02386241,0.009542054,0.003442844,0.0004864733,0.00005389359,0.0002304172,0.02129366],"genre_scores_gemma":[0.9889926,0.00003886593,0.006117033,0.004042029,0.0003311581,0.00002115187,0.000008127109,0.00002023522,0.0004288128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04795945,"threshold_uncertainty_score":0.7966564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0488473258141501,"score_gpt":0.3256763280032717,"score_spread":0.2768290021891217,"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."}}