{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005467742,0.0002670729,0.0003476637,0.0001616418,0.0007471844,0.0001344377,0.0001563408,0.0001086493,0.0000471702],"category_scores_gemma":[0.002292045,0.0002799572,0.00004993051,0.000560661,0.0001295316,0.0001269229,0.0005756369,0.0004083524,0.000008613247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001750358,"about_ca_system_score_gemma":0.0004975269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002156599,"about_ca_topic_score_gemma":0.0003440944,"domain_scores_codex":[0.9975904,0.000278733,0.0002896537,0.0009775085,0.0003560678,0.0005076186],"domain_scores_gemma":[0.9973196,0.001337516,0.0001182288,0.0004620224,0.0002523288,0.0005103509],"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":[0.000574576,0.0004421847,0.009891858,0.0003282246,0.0001729498,0.0008340997,0.001136245,0.02047927,0.004290087,0.00004968345,0.0004881986,0.9613127],"study_design_scores_gemma":[0.006594057,0.001282216,0.1061738,0.001979873,0.001003238,0.002094444,0.003177303,0.8158342,0.004383656,0.0003762048,0.05585062,0.001250425],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5693322,0.0004961345,0.4259765,0.002977299,0.0004993756,0.0003756868,0.0001688352,0.0001191223,0.00005479658],"genre_scores_gemma":[0.9609598,0.00001264525,0.002632015,0.03326276,0.001937979,0.000006731174,0.001140223,0.00003602035,0.00001183208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9600622,"threshold_uncertainty_score":0.9999653,"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."}}