{"id":"W4283798078","doi":"10.1007/s11517-022-02604-1","title":"Modeling global and local label correlation with graph convolutional networks for multi-label chest X-ray image classification","year":2022,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Correlation; Convolutional neural network; Artificial intelligence; Multi-label classification; Graph; Machine learning; Pattern recognition (psychology); Task (project management); Representation (politics); Computer-aided diagnosis; Data mining; Theoretical computer science; Mathematics","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":[0.0006728623,0.0002055191,0.0002292449,0.0000735818,0.0004151271,0.00008557425,0.0006089301,0.0001730693,0.00001432693],"category_scores_gemma":[0.0002545941,0.0001673913,0.00004171494,0.0005014263,0.0001755898,0.0001493572,0.0004929164,0.0004359004,0.000002556804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000130339,"about_ca_system_score_gemma":0.00005752411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009809092,"about_ca_topic_score_gemma":8.598592e-7,"domain_scores_codex":[0.9982244,0.00005558922,0.0003713742,0.0005693216,0.0003766505,0.0004026633],"domain_scores_gemma":[0.9992078,0.0002388095,0.0001026515,0.0002191813,0.0000829208,0.0001486645],"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":[0.00004520718,0.0003209866,0.00585675,0.00003326947,0.00004551472,0.000009660179,0.0000691671,0.6629829,0.000362798,0.1747591,0.0001229266,0.1553917],"study_design_scores_gemma":[0.001020379,0.0002018986,0.006725766,0.00002309575,0.000006193659,0.00002668024,0.00008313992,0.9911472,0.000002944187,0.0003920961,0.0001456883,0.0002248908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03469682,0.000361798,0.9628251,0.0008738623,0.0002597563,0.0002786864,0.000006053219,0.0006803608,0.00001762201],"genre_scores_gemma":[0.7711482,0.00001360295,0.2285465,0.0001263865,0.00004703758,0.00006391545,0.00004291284,0.000007170022,0.000004259203],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7364513,"threshold_uncertainty_score":0.6826018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0392016277018329,"score_gpt":0.2674742016426488,"score_spread":0.228272573940816,"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."}}