{"id":"W4367678680","doi":"10.3390/computers12050095","title":"Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases","year":2023,"lang":"en","type":"article","venue":"Computers","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Hyperparameter; Computer science; Transfer of learning; Convolutional neural network; Artificial intelligence; Deep learning; Receiver operating characteristic; Coronavirus disease 2019 (COVID-19); Machine learning; Precision and recall; F1 score; Pattern recognition (psychology); Medicine; Pathology; Infectious disease (medical specialty); Disease","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":[0.001240448,0.001166591,0.0005670511,0.000943177,0.0003004699,0.0008752684,0.001292547,0.001184109,0.001039542],"category_scores_gemma":[0.003614035,0.0004385173,0.0005761966,0.0005323455,0.0005314957,0.001321547,0.001131497,0.001404971,0.0005578647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001121403,"about_ca_system_score_gemma":0.001055069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01366872,"about_ca_topic_score_gemma":0.01480748,"domain_scores_codex":[0.9995722,0.0001296736,0.0000239814,0.0001226473,0.00008657936,0.00006493053],"domain_scores_gemma":[0.9990873,0.0004959852,0.00008501215,0.00009219061,0.0001876391,0.00005182899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000382302,0.0003114574,0.02321562,0.0001553637,0.0002181283,0.0003688834,0.0001579953,0.669049,0.01130615,0.004872475,0.006227948,0.2837346],"study_design_scores_gemma":[0.000004975102,0.00002620596,0.0006026463,0.00001024043,0.00001109793,0.00002739826,0.000008690604,0.9954183,0.001338802,0.00215846,0.0003880832,0.00000503853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3463921,0.003590774,0.6360787,0.002842395,0.0002841695,0.0001537232,0.0009640784,0.004354988,0.005339007],"genre_scores_gemma":[0.91621,0.0006945913,0.07824068,0.0006581146,0.0001064385,0.0000689517,0.001189564,0.00006911036,0.002762548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01366872,"threshold_uncertainty_score":0.02717829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03803358597651233,"score_gpt":0.3080784421247301,"score_spread":0.2700448561482177,"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."}}