{"id":"W2564373493","doi":"10.1016/j.cmpb.2016.12.007","title":"A ℓ2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD","year":2016,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"China Postdoctoral Science Foundation; Fundamental Research Funds for the Central Universities; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Artificial intelligence; Kernel (algebra); Multiple kernel learning; Norm (philosophy); Computer science; Discriminative model; Pattern recognition (psychology); Algorithm; Feature (linguistics); Kernel method; Mathematics; Support vector machine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.005364881,0.0005738242,0.001160271,0.000929555,0.0004571202,0.001112276,0.001607914,0.001483513,0.0009205987],"category_scores_gemma":[0.01245274,0.0003311707,0.0007154857,0.0005765769,0.0007299971,0.001308798,0.00135033,0.001310489,0.0003418638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007024352,"about_ca_system_score_gemma":0.001460659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003385704,"about_ca_topic_score_gemma":0.002685576,"domain_scores_codex":[0.9982477,0.0007080806,0.0001427896,0.0002816392,0.0004892286,0.0001305631],"domain_scores_gemma":[0.9956313,0.002241854,0.0002979168,0.0004860449,0.001211351,0.0001315119],"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":[0.001595136,0.0005275389,0.006585096,0.000343176,0.0002404157,0.0001071043,0.0001603436,0.2818962,0.01682682,0.008399053,0.005721029,0.6775981],"study_design_scores_gemma":[0.00001027967,0.00007404393,0.0007583548,0.000007940683,0.00001998529,0.00004031012,0.00001021501,0.9949241,0.002530944,0.001310159,0.0003062653,0.000007398135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08628731,0.001086603,0.9099311,0.0004947076,0.00007569319,0.00007101527,0.0001517082,0.001133718,0.000768164],"genre_scores_gemma":[0.7326297,0.0003705345,0.2633573,0.0001872795,0.00007694619,0.00009674529,0.000479676,0.0001406691,0.002661209],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005364881,"threshold_uncertainty_score":0.02837253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06469870305100363,"score_gpt":0.4111201058653698,"score_spread":0.3464214028143662,"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."}}