{"id":"W4251921437","doi":"10.1016/j.jalz.2011.05.918","title":"P2‐028: Influence of the training library composition on a patch‐based label fusion method: Application to hippocampus segmentation on the ADNI dataset","year":2011,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"NeuroRx Research (Canada); Montreal Neurological Institute and Hospital","funders":"","keywords":"Segmentation; Atrophy; Population; Kappa; Artificial intelligence; Dice; Computer science; Temporal lobe; Pattern recognition (psychology); Neuroimaging; Hippocampus; Alzheimer's disease; Medicine; Pathology; Psychology; Neuroscience; Internal medicine; Disease; Mathematics; 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":[0.000353596,0.0001612264,0.0001089684,0.00009432786,0.0003498948,0.00004357953,0.000468628,0.00005373454,0.0001345419],"category_scores_gemma":[0.00005719201,0.0001107012,0.00004873405,0.000538954,0.00008601557,0.0002585765,0.0000759931,0.0001718498,0.000111197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007206437,"about_ca_system_score_gemma":0.00004035348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004448699,"about_ca_topic_score_gemma":0.000006535121,"domain_scores_codex":[0.9981351,0.0005471118,0.0003238324,0.0004290479,0.0003839095,0.0001810256],"domain_scores_gemma":[0.9986694,0.0002863071,0.000308584,0.0006430935,0.0000287124,0.00006384686],"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":[0.0002200005,0.0002355567,0.0001588914,0.000004355168,0.00007728917,6.07376e-7,0.0009624119,0.0003684921,0.9060511,0.005262562,0.001282604,0.08537614],"study_design_scores_gemma":[0.0003366776,0.000208606,0.01095806,0.00003187731,0.0003993769,0.000002267353,0.0001124526,0.004375048,0.9810036,0.0006560019,0.001780316,0.0001357054],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9737518,0.0001110526,0.01561726,0.005113828,0.0003992941,0.002495527,0.0004936618,0.000194772,0.001822754],"genre_scores_gemma":[0.9858444,0.000004285485,0.002682185,0.01105243,0.00002430106,0.0002401434,0.0001281991,0.00002160763,0.000002402087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08524043,"threshold_uncertainty_score":0.4514262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09187535610453887,"score_gpt":0.3046359415237745,"score_spread":0.2127605854192356,"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."}}