{"id":"W3015021600","doi":"10.1016/j.patrec.2020.09.010","title":"COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images","year":2020,"lang":"en","type":"preprint","venue":"Pattern Recognition Letters","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Convolutional neural network; Identification (biology); Artificial intelligence; Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Sensitivity (control systems); Artificial neural network; Pattern recognition (psychology); Machine learning; Disease; Medicine; Pathology; Engineering; Infectious disease (medical specialty); Biology","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":[0.0006675601,0.000638922,0.00115022,0.0003803331,0.0001822745,0.0001564013,0.0004040022,0.0005970192,0.0008782914],"category_scores_gemma":[0.007774605,0.0007290596,0.0006943417,0.0003203296,0.0002534907,0.00009600733,0.0002076771,0.0009677574,0.0001271823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008385907,"about_ca_system_score_gemma":0.00122759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002518339,"about_ca_topic_score_gemma":0.00006316557,"domain_scores_codex":[0.9955378,0.0003827367,0.001371824,0.00150634,0.0006696472,0.0005317107],"domain_scores_gemma":[0.9924347,0.003909491,0.001490823,0.001061474,0.0003620788,0.0007413939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002004309,0.001538938,0.02250742,0.02752128,0.002374246,0.001110513,0.003163295,0.04407118,0.0800183,0.000006319589,0.7769408,0.03874342],"study_design_scores_gemma":[0.05922835,0.003771841,0.09113844,0.06083359,0.03811028,0.0002151629,0.002029854,0.1176228,0.3439645,0.07900324,0.187107,0.01697496],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07521695,0.0002949879,0.6667916,0.2461924,0.001271973,0.002646589,0.007245013,0.0003385238,0.00000193957],"genre_scores_gemma":[0.5580354,0.0001106712,0.02033111,0.4032424,0.002050484,0.001558925,0.01449962,0.0001652022,0.000006156657],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6464605,"threshold_uncertainty_score":0.9995161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1005078118989475,"score_gpt":0.3616092688494228,"score_spread":0.2611014569504753,"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."}}