{"id":"W4403512568","doi":"10.1093/neuonc/noae144.188","title":"P10.12.B DEEP LEARNING-BASED NORMALIZATION AND UNMIXING OF HYPERSPECTRAL IMAGES FOR BRAIN TUMOR SURGERY","year":2024,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hyperspectral imaging; Normalization (sociology); Artificial intelligence; Spatial normalization; Computer science; Deep learning; Computer vision; Pattern recognition (psychology); Medicine; Sociology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000660664,0.0008189931,0.0002630075,0.000367954,0.000221927,0.0005457511,0.001089093,0.0007827042,0.007986929],"category_scores_gemma":[0.001261401,0.0003014806,0.0004041148,0.0003312685,0.0003965667,0.0008173937,0.0008407311,0.001531571,0.00294664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005538001,"about_ca_system_score_gemma":0.001239651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005217383,"about_ca_topic_score_gemma":0.005653415,"domain_scores_codex":[0.999836,0.00002499134,0.000006526093,0.00004149018,0.00006322685,0.00002773861],"domain_scores_gemma":[0.9996581,0.00007201886,0.00003497933,0.000049377,0.0001557283,0.00002969787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004364132,0.0003800272,0.002028986,0.000258415,0.0001148166,0.0001591633,0.00009731232,0.3620884,0.04167432,0.01089625,0.03918481,0.5426811],"study_design_scores_gemma":[0.000009150654,0.00002988795,0.0002723366,0.00001013327,0.000005329209,0.0000307093,0.000004798641,0.9800603,0.0127813,0.001919069,0.004869364,0.00000746046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01933868,0.0004128097,0.9620589,0.000505633,0.0001571864,0.0001004966,0.000586692,0.01164629,0.005193257],"genre_scores_gemma":[0.3672765,0.0008707091,0.6026897,0.0004521431,0.0001031876,0.0004874649,0.003741036,0.001574728,0.02280453],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007986929,"threshold_uncertainty_score":0.02671897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03192292005977731,"score_gpt":0.2928585492081994,"score_spread":0.2609356291484221,"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."}}