{"id":"W4200246746","doi":"10.3390/bdcc5040073","title":"Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture","year":2021,"lang":"en","type":"article","venue":"Big Data and Cognitive Computing","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut du Savoir Montfort; Université Laval; Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency; Université de Moncton; New Brunswick Innovation Foundation; Microsoft","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Coronavirus disease 2019 (COVID-19); Deep learning; Medical imaging; Workflow; Pneumonia; Generalization; Computer-aided diagnosis; Process (computing); Radiography; Binary classification; Pattern recognition (psychology); Machine learning; Radiology; Medicine; Pathology; Database; Support vector machine; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003554571,0.0007677949,0.0003794182,0.0004422098,0.0002203413,0.0004456946,0.0009284441,0.0008874328,0.000960853],"category_scores_gemma":[0.0007588349,0.0002604786,0.0005731557,0.0002402543,0.0002483269,0.0004780062,0.0006624635,0.0008013716,0.0002568715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007302636,"about_ca_system_score_gemma":0.0007523169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01248044,"about_ca_topic_score_gemma":0.01448621,"domain_scores_codex":[0.9998583,0.00001666019,0.000008338227,0.00004700138,0.00003338161,0.000036288],"domain_scores_gemma":[0.9998025,0.00007100716,0.00002866061,0.00002062284,0.00006056716,0.00001667949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000540655,0.000365957,0.01249306,0.0001065147,0.0001822857,0.0006967963,0.0001214596,0.4755259,0.03246596,0.002019703,0.004794355,0.4706874],"study_design_scores_gemma":[0.000005183852,0.00003459468,0.001238347,0.000005507683,0.0000115869,0.00005141522,0.0000064407,0.9952106,0.002724309,0.0004448955,0.0002617992,0.000005431439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3209683,0.001036412,0.6694967,0.0008326677,0.00009642607,0.0001420231,0.0006609856,0.003913069,0.002853326],"genre_scores_gemma":[0.8828361,0.0004059972,0.1100481,0.0002973505,0.00003666328,0.00008668865,0.001606213,0.00005892643,0.004623926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01248044,"threshold_uncertainty_score":0.02481556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1369079150930947,"score_gpt":0.3548038088738674,"score_spread":0.2178958937807726,"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."}}