{"id":"W3212465629","doi":"10.18280/ria.350509","title":"Unsupervised Convolutional Filter Learning for COVID-19 Classification","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoencoder; Coronavirus disease 2019 (COVID-19); Computer science; Artificial intelligence; Convolutional neural network; Filter (signal processing); Identification (biology); Unsupervised learning; Machine learning; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Deep learning; Sensitivity (control systems); Pattern recognition (psychology); Medicine; Engineering; Pathology; Computer vision; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004820186,0.0001719252,0.0002878625,0.0001283821,0.0003145095,0.00005715902,0.0001284366,0.0001367088,0.001851162],"category_scores_gemma":[0.004087728,0.0001869622,0.0002170943,0.0004784355,0.0001243258,0.00008558152,0.00005506039,0.0002553255,0.0004327691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003742487,"about_ca_system_score_gemma":0.0008008349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002938134,"about_ca_topic_score_gemma":0.00001384715,"domain_scores_codex":[0.9982442,0.00009217981,0.0004809735,0.0006020117,0.0002298293,0.0003507704],"domain_scores_gemma":[0.9974842,0.001164444,0.0001133777,0.0004792653,0.0004586042,0.0003001145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001050919,0.003086894,0.03384776,0.004241075,0.0003944563,0.0003671391,0.007980184,0.2333607,0.425594,0.04983773,0.1675929,0.07264623],"study_design_scores_gemma":[0.0002793006,0.0001447946,0.0008232893,0.0001478426,0.00008040816,0.00008829188,0.001209421,0.3757106,0.05701195,0.0008067079,0.5634841,0.0002133442],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04767631,0.001150526,0.779941,0.1676359,0.000689006,0.00120964,0.00003509552,0.0003869123,0.001275608],"genre_scores_gemma":[0.9606204,0.0002072367,0.004656358,0.02195953,0.0003880941,0.0002317461,0.0004176859,0.00005006148,0.01146888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9129441,"threshold_uncertainty_score":0.9990613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1471766052982907,"score_gpt":0.3721287270168919,"score_spread":0.2249521217186012,"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."}}