{"id":"W3198552619","doi":"10.1166/jmihi.2021.3850","title":"Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Imaging and Health Informatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Classifier (UML); Coronavirus disease 2019 (COVID-19); Pattern recognition (psychology); Deep learning; Feature extraction; Segmentation; Machine learning; Medical imaging; Computer-aided diagnosis; Artificial neural network; Medicine; Pathology; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002745224,0.0001277291,0.0004655761,0.0001748466,0.0002051652,0.00006861529,0.00004697829,0.00005321419,0.00003301418],"category_scores_gemma":[0.001526493,0.0001085976,0.00004923724,0.0002022815,0.0001939345,0.0002457328,0.00005166507,0.0005885671,2.825612e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002263192,"about_ca_system_score_gemma":0.00131612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002887903,"about_ca_topic_score_gemma":0.00001244251,"domain_scores_codex":[0.9974591,0.000193207,0.001318321,0.00009435178,0.0007001722,0.0002348485],"domain_scores_gemma":[0.9973613,0.0004902547,0.001092873,0.00008845409,0.000221101,0.0007460035],"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":[0.0000735186,0.0003001569,0.2324379,0.01143408,0.00007161458,0.0001151259,0.008146195,0.001011007,0.0003861023,0.00001854649,0.004131481,0.7418743],"study_design_scores_gemma":[0.002909022,0.0001283381,0.03059956,0.001591333,0.0001111326,0.001063388,0.003547015,0.9570341,0.00002451256,0.00001346366,0.002896917,0.00008118745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6443214,0.005872476,0.2161681,0.1328698,0.0004001085,0.0003176888,0.000003385903,0.0000373669,0.000009634966],"genre_scores_gemma":[0.5891207,0.007889278,0.3483267,0.05419404,0.0003772111,0.000005549373,0.00003917813,0.0000387624,0.000008625469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9560231,"threshold_uncertainty_score":0.4428482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08638902791999763,"score_gpt":0.4316482316699142,"score_spread":0.3452592037499166,"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."}}