{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002563907,0.0002515857,0.0006468649,0.0005254697,0.00006925991,0.00002708047,0.00009460382,0.0001942795,0.000006767019],"category_scores_gemma":[0.0004420215,0.0001731004,0.00008695347,0.0006272712,0.0002595474,0.00009745848,0.0001059506,0.0002616498,0.000001112163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002402026,"about_ca_system_score_gemma":0.00006830391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002886077,"about_ca_topic_score_gemma":0.00002037078,"domain_scores_codex":[0.9978399,0.0003730346,0.0005297152,0.0006437516,0.0001793834,0.0004342688],"domain_scores_gemma":[0.9984717,0.0007833884,0.0001498285,0.0002646974,0.0001543409,0.0001760627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004346689,0.0004233716,0.01755325,0.0003249588,0.00005720985,0.00004093324,0.0017441,0.000003862218,0.03501989,0.00004997251,0.0002132907,0.9441345],"study_design_scores_gemma":[0.1015685,0.01003596,0.3989961,0.0236806,0.0007827836,0.0008751898,0.0008951275,0.300257,0.02114612,0.002319732,0.1374263,0.002016567],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1542452,0.00118273,0.8198671,0.0221629,0.0006322155,0.001779687,0.000003360493,0.0001210339,0.000005795213],"genre_scores_gemma":[0.1088267,0.0002522299,0.8885133,0.001296342,0.0005097466,0.000254598,0.00005281308,0.00004787927,0.0002464672],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9421179,"threshold_uncertainty_score":0.7058828,"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."}}