{"id":"W3106791009","doi":"10.1109/access.2020.3040245","title":"A Two-Dimensional Sparse Matrix Profile DenseNet for COVID-19 Diagnosis Using Chest CT Images","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; Research Institute in Oncology and Hematology; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Artificial intelligence; Preprocessor; Pattern recognition (psychology); Computer science; Coronavirus disease 2019 (COVID-19); Pixel; Anomaly (physics); Matrix (chemical analysis); Computer vision; Medicine; Pathology; Physics; Infectious disease (medical specialty); Disease","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003085415,0.0003507976,0.0006013407,0.0001815409,0.0002489585,0.0001776781,0.0003914715,0.00008620379,0.0005626364],"category_scores_gemma":[0.002082637,0.000337906,0.0002423152,0.0005684781,0.0001384541,0.0003319046,0.0001958087,0.0002411642,0.00008648259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003828075,"about_ca_system_score_gemma":0.001064364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001122892,"about_ca_topic_score_gemma":0.00004827493,"domain_scores_codex":[0.9975906,0.00008084987,0.0004835058,0.0008012186,0.0004989835,0.0005448121],"domain_scores_gemma":[0.9969686,0.001350819,0.0002294974,0.0004511312,0.0002200456,0.0007799662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001118106,0.0008334334,0.1256043,0.003040299,0.0003292829,0.001382114,0.0004638051,0.0280681,0.04654422,0.00004698868,0.7905613,0.002008024],"study_design_scores_gemma":[0.02058537,0.001051597,0.01420168,0.001086911,0.002310824,0.000580869,0.0001985561,0.0859535,0.5821779,0.0004996884,0.2892175,0.002135577],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8823141,0.0006310936,0.002996163,0.1101284,0.0005644899,0.002503574,0.0003736346,0.0004362979,0.00005221349],"genre_scores_gemma":[0.8750282,0.00003337418,0.01073161,0.1120709,0.001283022,0.0005432831,0.00009546971,0.0001169844,0.00009712595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5356337,"threshold_uncertainty_score":0.9999073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.14038053678406,"score_gpt":0.4288107679487088,"score_spread":0.2884302311646487,"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."}}