{"id":"W1914116674","doi":"10.1007/s11548-015-1311-1","title":"Within-brain classification for brain tumor segmentation","year":2015,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Support vector machine; Generalization; Machine learning; Brain tumor; Kernel (algebra); Pattern recognition (psychology); Feature (linguistics); Adaptation (eye); Noise (video); Image (mathematics)","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":[0.001372177,0.0001122674,0.0002300223,0.000354937,0.00007601216,0.00008348127,0.0002038955,0.00007097577,0.00000829913],"category_scores_gemma":[0.001424107,0.00009845317,0.0001222097,0.0001229823,0.0001328301,0.0003068687,0.00001976397,0.0001633554,0.000004489322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001027551,"about_ca_system_score_gemma":0.0001719597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001212877,"about_ca_topic_score_gemma":0.000001241641,"domain_scores_codex":[0.9983529,0.0003961288,0.0006277987,0.0002120458,0.0002866009,0.0001245519],"domain_scores_gemma":[0.9963951,0.002261929,0.0007254975,0.00008600612,0.0004045434,0.0001269488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003356107,0.0006202591,0.01708495,0.000046865,0.000356869,0.0003073626,0.002653641,0.001054952,0.4578066,0.02582927,0.2568423,0.2340408],"study_design_scores_gemma":[0.01422183,0.002226908,0.3211781,0.0004024564,0.0001688937,0.05458935,0.002266884,0.2484954,0.1514673,0.02732099,0.1759409,0.001720972],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7195257,0.00007236389,0.2429267,0.03035549,0.006752749,0.0001794419,0.00001203917,0.00004502988,0.0001304584],"genre_scores_gemma":[0.9885945,0.00001287134,0.003494109,0.00690564,0.0008291999,0.00001324414,0.00001384069,0.00001193404,0.0001246108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3063393,"threshold_uncertainty_score":0.4014804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08643900594461627,"score_gpt":0.3172874051676718,"score_spread":0.2308483992230555,"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."}}