{"id":"W4220939135","doi":"10.18280/ria.360105","title":"Deep Learning-Based Segmentation and Classification of COVID-19 Infection Severity Levels from CT Scans","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Classifier (UML); Deep learning; Categorization; Computer science; Coronavirus disease 2019 (COVID-19); Feature extraction; Segmentation; Machine learning; Pattern recognition (psychology); Support vector machine; Medicine; Disease; Infectious disease (medical specialty); Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006613847,0.001009216,0.000745105,0.001848632,0.0002485548,0.0008584183,0.0006378706,0.0008697241,0.0008529319],"category_scores_gemma":[0.001709726,0.0003503445,0.0009280041,0.0006513706,0.000326153,0.0007167623,0.0008090389,0.0009343482,0.0004742646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005469572,"about_ca_system_score_gemma":0.0008130306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006845875,"about_ca_topic_score_gemma":0.008491676,"domain_scores_codex":[0.9996136,0.00005684772,0.00003927151,0.0001082192,0.00007248005,0.0001095919],"domain_scores_gemma":[0.9996095,0.0001041455,0.00007237642,0.00003153562,0.000118434,0.00006400013],"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":[0.002027869,0.0006270712,0.1285882,0.0004008996,0.0003174404,0.002065926,0.0004583116,0.2088077,0.05998105,0.002863638,0.01606378,0.5777982],"study_design_scores_gemma":[0.00001971807,0.0001457919,0.01411253,0.00004886714,0.00005687841,0.00037139,0.00009971349,0.9722046,0.01031658,0.001550558,0.001046443,0.00002691141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5919574,0.002528509,0.3953298,0.0009351943,0.0001764695,0.0003018299,0.002691773,0.002607805,0.00347122],"genre_scores_gemma":[0.9135787,0.001074884,0.07760113,0.0003352468,0.0001030455,0.0001288727,0.005133492,0.00009112274,0.001953466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006845875,"threshold_uncertainty_score":0.01361203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0853647289588381,"score_gpt":0.3499247576156842,"score_spread":0.2645600286568461,"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."}}