{"id":"W6893288205","doi":"10.5281/zenodo.15244852","title":"Brain tumor segmentation using deep neural image analysis","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":"Lambton College","funders":"","keywords":"Interpretability; Convolutional neural network; Deep learning; Segmentation; Medical imaging; Task (project management); Feature (linguistics); Pattern recognition (psychology)","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":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004644099,0.0001275614,0.0001145141,0.0005790585,0.001544394,0.001689428,0.0004627275,0.00003211364,0.008829448],"category_scores_gemma":[0.0007859906,0.0001339192,0.0001028223,0.002456417,0.0001483654,0.0005417528,0.0002624041,0.0002323856,0.003695438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002041079,"about_ca_system_score_gemma":0.000003196309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001023915,"about_ca_topic_score_gemma":5.054162e-7,"domain_scores_codex":[0.9980503,0.0004928363,0.0002444716,0.0005612228,0.0003750614,0.0002760859],"domain_scores_gemma":[0.9992769,0.00006727826,0.00007682245,0.000309802,0.0001436758,0.0001255096],"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":[0.00002772507,0.00005470572,0.000002004531,0.00004616274,0.00003983427,0.0000447944,0.0008837973,0.0004990969,0.9311296,0.003041489,0.007481896,0.05674887],"study_design_scores_gemma":[0.0003666353,0.0001443206,0.0006891823,0.00001839953,0.0001453646,0.0004523203,0.0007413905,0.5293542,0.07664846,0.0002589603,0.3908322,0.0003485729],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6530207,0.0001415848,0.249938,0.005871996,0.0008453202,0.001280806,0.0003611881,0.006167873,0.08237261],"genre_scores_gemma":[0.9976892,0.000007925469,0.0003217073,0.0005292623,0.0001101623,6.583618e-8,0.0002457787,0.0005215196,0.0005743353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8544812,"threshold_uncertainty_score":0.9997554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05562991909773523,"score_gpt":0.2945632270982297,"score_spread":0.2389333080004945,"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."}}