{"id":"W4200092864","doi":"10.1109/embc46164.2021.9629846","title":"Deep Learning and Binary Relevance Classification of Multiple Diseases using Chest X-Ray images","year":2021,"lang":"en","type":"article","venue":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Convolutional neural network; Relevance (law); Artificial intelligence; CAD; Computer science; Lung cancer; Deep learning; Radiology; Variety (cybernetics); Second opinion; Machine learning; Medical diagnosis; Medicine; Computer-aided diagnosis; Clinical significance; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003104781,0.0001869069,0.0004402849,0.0001324159,0.00004781298,0.000007950295,0.0002065395,0.0001473458,0.0001137413],"category_scores_gemma":[0.003241361,0.0001529103,0.0001304064,0.0003957499,0.0004150078,0.0000911052,0.0001268559,0.0004254929,0.00000111458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001023582,"about_ca_system_score_gemma":0.0001558165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001599409,"about_ca_topic_score_gemma":0.0000306536,"domain_scores_codex":[0.9986733,0.00008929329,0.0004641342,0.0003463757,0.0002350974,0.0001917513],"domain_scores_gemma":[0.9979703,0.0008671483,0.0002366132,0.000244188,0.0006125114,0.00006925545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005854245,0.0001797167,0.1469944,0.0003658523,0.0002213902,0.000005078755,0.00166862,0.01386634,0.83395,0.0001786278,0.001481053,0.001030327],"study_design_scores_gemma":[0.003858362,0.0002955568,0.5218193,0.004296922,0.0003504704,0.00005883072,0.003920827,0.4266807,0.01226906,0.0001578313,0.0257918,0.0005002782],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745871,0.001950518,0.0114935,0.01076192,0.0008822482,0.0001962353,0.00004798468,0.00003543786,0.00004508157],"genre_scores_gemma":[0.9903479,0.002252613,0.006451691,0.0003347733,0.0002480169,0.00001296639,0.0001008685,0.00002013723,0.0002310558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.821681,"threshold_uncertainty_score":0.6235499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05038633689486323,"score_gpt":0.340934946790207,"score_spread":0.2905486098953438,"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."}}