{"id":"W6911901931","doi":"10.5281/zenodo.14147267","title":"A survey on brain MRI segmentation","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Deep learning; Convolutional neural network; Pattern recognition (psychology); Ground truth; Magnetic resonance imaging; Image segmentation; Data set; Medical imaging","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":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008348532,0.0001099105,0.00007466727,0.0002684499,0.001275418,0.001176747,0.0004479287,0.00004403517,0.007202864],"category_scores_gemma":[0.002123031,0.0001113453,0.0000400799,0.0009250097,0.0001053578,0.0002440573,0.0001853488,0.0002514187,0.02197423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001591754,"about_ca_system_score_gemma":0.000003820402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009442846,"about_ca_topic_score_gemma":5.917167e-7,"domain_scores_codex":[0.9979556,0.0007480573,0.0001827622,0.0005157065,0.0003688365,0.0002290748],"domain_scores_gemma":[0.9992146,0.0002194354,0.00004650321,0.0002960331,0.0001149758,0.0001084699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007115505,0.0001055527,0.0000014473,0.0000403596,0.000008725327,0.00001932574,0.0007639361,0.00005927399,0.2453199,0.01373542,0.5465441,0.1933308],"study_design_scores_gemma":[0.0002172303,0.0002156917,0.001812132,0.0000270203,0.000003510198,0.00006309359,0.00008696934,0.003528359,0.03782221,0.0002156593,0.9558626,0.0001454616],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1967405,0.0001844691,0.0651757,0.03796222,0.002815207,0.003078819,0.00193722,0.01452524,0.6775806],"genre_scores_gemma":[0.9936099,0.00003631436,0.00002918531,0.001177919,0.0001098552,8.287908e-8,0.0006002629,0.0007614851,0.003675002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7968693,"threshold_uncertainty_score":0.9998601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07965135515159145,"score_gpt":0.2949670806714111,"score_spread":0.2153157255198197,"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."}}