{"id":"W4292171780","doi":"10.1186/s12859-022-04878-6","title":"Semi-supervised COVID-19 CT image segmentation using deep generative models","year":2022,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; University of Manitoba","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Deep learning; Autoencoder; Pattern recognition (psychology); Generative model; Image segmentation; Convolutional neural network; Ground truth; Scale-space segmentation; Segmentation-based object categorization; Generative grammar","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005214711,0.0002476819,0.0003424947,0.0003069682,0.0005880378,0.0000870228,0.0001804194,0.00004370867,0.0005718765],"category_scores_gemma":[0.0002905997,0.0002511932,0.0001506951,0.0005365912,0.00008399653,0.0005463127,0.0002592973,0.0002815327,0.00004018765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001607282,"about_ca_system_score_gemma":0.001188501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001696715,"about_ca_topic_score_gemma":0.00002580209,"domain_scores_codex":[0.9979503,0.0001183856,0.000641523,0.0002234225,0.0007074542,0.0003588989],"domain_scores_gemma":[0.9985847,0.000266246,0.0002583323,0.0004631327,0.0001109927,0.0003165818],"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":[0.0002057545,0.0003805562,0.002414839,0.001637865,0.0001319706,0.00007920802,0.02126012,0.9451956,0.004825052,0.0003641469,0.02117229,0.00233259],"study_design_scores_gemma":[0.00238301,0.0001498158,0.00005192482,0.00002943594,0.0001705051,0.0001916657,0.006183383,0.9842201,0.00124407,0.0002545649,0.004850834,0.0002706752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07721895,0.0001620999,0.9163527,0.003630862,0.0003163194,0.001359183,0.0001421702,0.0002848111,0.0005329408],"genre_scores_gemma":[0.04155819,0.00005301893,0.866474,0.0905064,0.0001827783,0.0002192125,0.0007120097,0.00008069458,0.000213672],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08687554,"threshold_uncertainty_score":0.999994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08687116947554052,"score_gpt":0.3473627083257685,"score_spread":0.260491538850228,"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."}}