{"id":"W3149262220","doi":"10.21203/rs.3.rs-600363/v1","title":"TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design For Detection of Tuberculosis Cases From Chest X-Ray Images","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thunder Bay Regional Health Sciences Centre; McMaster University; University of Waterloo","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Deep learning; Tuberculosis; Machine learning; Medicine; Computer science; Cohort; Artificial neural network; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002056171,0.000432416,0.0008881229,0.0005449971,0.0003739777,0.0001910476,0.0003075006,0.0006235947,0.0002548783],"category_scores_gemma":[0.003455318,0.0004464049,0.0006137737,0.0007453688,0.0002592341,0.0001450544,0.000626384,0.001423239,0.00001606806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001078899,"about_ca_system_score_gemma":0.001288701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003559709,"about_ca_topic_score_gemma":0.0004243839,"domain_scores_codex":[0.9943928,0.001194448,0.0007310899,0.001202524,0.001598597,0.0008805856],"domain_scores_gemma":[0.9902477,0.005467906,0.0002810219,0.0009635565,0.002724191,0.0003155904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.01227789,0.009357599,0.1141622,0.04633791,0.008550746,0.002180258,0.004151247,0.3901646,0.2665751,0.00008316727,0.07806329,0.06809606],"study_design_scores_gemma":[0.007499045,0.004195674,0.4776584,0.01305742,0.002457516,0.0001403863,0.002679903,0.4345152,0.04962494,0.001352756,0.005211788,0.001606927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8660676,0.01446406,0.09655578,0.01224737,0.001322355,0.008219933,0.0006773546,0.0004269407,0.00001857851],"genre_scores_gemma":[0.9776,0.0009351384,0.01608136,0.0002878359,0.001613882,0.001693226,0.001618855,0.0001159812,0.00005366653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3634962,"threshold_uncertainty_score":0.9997988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08359636210538827,"score_gpt":0.3844175795535964,"score_spread":0.3008212174482081,"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."}}