{"id":"W4214916274","doi":"10.3390/info13030124","title":"An Attentive Multi-Modal CNN for Brain Tumor Radiogenomic Classification","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Brain tumor; Modal; Feature (linguistics); Modality (human–computer interaction); Pattern recognition (psychology); Feature extraction; Process (computing); Magnetic resonance imaging; Embedding; Deep learning; Medical imaging; Image (mathematics); Modalities; Radiology; Medicine; Pathology","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":[0.0004358476,0.000920868,0.0004988658,0.0005176952,0.0002373223,0.0005117901,0.001475345,0.0009552581,0.001489685],"category_scores_gemma":[0.0007350865,0.0003219883,0.0006628326,0.0003739391,0.0003319561,0.0008923855,0.000756661,0.001032234,0.0004522895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008786153,"about_ca_system_score_gemma":0.000570705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009244828,"about_ca_topic_score_gemma":0.01179643,"domain_scores_codex":[0.9998426,0.0000204524,0.0000067877,0.00005743562,0.00003274862,0.00003988775],"domain_scores_gemma":[0.9998367,0.00004126344,0.00001737899,0.00002309994,0.0000642651,0.00001724243],"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":[0.0003936716,0.0003341852,0.003377737,0.0001290319,0.0002261441,0.0002705114,0.00009005574,0.3156746,0.03551363,0.003766673,0.009630947,0.6305929],"study_design_scores_gemma":[0.000003561181,0.00003484306,0.0003160365,0.000005585325,0.0000185048,0.0000302971,0.00000477093,0.9953773,0.002811887,0.0009566539,0.0004355686,0.000004970391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.132735,0.002377192,0.8543277,0.0008245403,0.0002466895,0.0001229209,0.000529939,0.004478257,0.004357856],"genre_scores_gemma":[0.8306822,0.0007944272,0.1568436,0.0008245962,0.0001306888,0.00009906073,0.001414147,0.0001166046,0.009094621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009244828,"threshold_uncertainty_score":0.01838201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01990724538427908,"score_gpt":0.3133428723991584,"score_spread":0.2934356270148793,"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."}}