{"id":"W2737365645","doi":"10.2991/bbe-17.2017.81","title":"Cerebral Microbleed Detection by Wavelet Entropy and Naive Bayes Classifier","year":2017,"lang":"en","type":"article","venue":"","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Government of Jiangsu Province","keywords":"Naive Bayes classifier; Artificial intelligence; Pattern recognition (psychology); Computer science; Bayes error rate; Wavelet; Entropy (arrow of time); Bayes classifier; Machine learning; Support vector machine; Physics","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":[0.00009268747,0.0001173367,0.00009723339,0.00004659495,0.0008149884,0.0003514368,0.000187598,0.00007636581,0.0002471484],"category_scores_gemma":[0.0002950424,0.0001027669,0.00003616539,0.00004694486,0.0002198655,0.0003511535,0.00005865991,0.0001424115,0.000117997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003541503,"about_ca_system_score_gemma":0.000009446498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005242915,"about_ca_topic_score_gemma":0.00003164777,"domain_scores_codex":[0.9990554,0.00006729893,0.000141414,0.0004054288,0.0001327534,0.0001976921],"domain_scores_gemma":[0.9993222,0.00005818244,0.0001457062,0.0003581945,0.00002428662,0.00009148224],"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":[0.00002246676,0.00002130162,0.0002407736,0.000004114847,0.000001957521,0.000001124625,0.00004013397,4.636361e-8,0.973042,0.001698186,0.001735256,0.02319261],"study_design_scores_gemma":[0.0004945713,0.00005471869,0.01377561,0.000003470093,0.000005532644,0.00003849461,0.00006520883,0.002482558,0.9592698,0.0005534948,0.02311478,0.0001418182],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9656595,0.00001366915,0.007137,0.003573448,0.0006551796,0.0002695245,0.00001749976,0.0002091141,0.02246508],"genre_scores_gemma":[0.99037,0.00002266582,0.0001070146,0.0007367246,0.0000605221,0.00001581479,0.000001293139,0.00001346423,0.008672554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02471046,"threshold_uncertainty_score":0.6268314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02625069499361481,"score_gpt":0.2541738581153578,"score_spread":0.2279231631217429,"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."}}