{"id":"W4281712277","doi":"10.3390/jcm11113013","title":"Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-rays","year":2022,"lang":"en","type":"article","venue":"Journal of Clinical Medicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency","keywords":"Medicine; Coronavirus disease 2019 (COVID-19); Convolutional neural network; Context (archaeology); Artificial intelligence; Pneumonia; Deep learning; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Radiography; Machine learning; Pattern recognition (psychology); Radiology; Computer science; Pathology; Infectious disease (medical specialty); Internal medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007377062,0.0008074147,0.0004533888,0.0007796049,0.0001878443,0.0009367491,0.0008899909,0.0009285914,0.001608368],"category_scores_gemma":[0.003218266,0.0002253951,0.000783201,0.0003069513,0.0005141099,0.0008049231,0.0007048341,0.0009678741,0.0004285709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008567868,"about_ca_system_score_gemma":0.0005922299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008221831,"about_ca_topic_score_gemma":0.004822371,"domain_scores_codex":[0.9997023,0.0000762849,0.00001407376,0.00009083019,0.00006401761,0.00005255845],"domain_scores_gemma":[0.9994,0.0003141102,0.00007778392,0.00006247205,0.0001162772,0.00002931163],"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":[0.000696882,0.0001829359,0.006836752,0.000290831,0.0001498388,0.0004684828,0.0001262351,0.4972355,0.03222062,0.01209808,0.004190243,0.4455037],"study_design_scores_gemma":[0.000007810982,0.00006704705,0.001262125,0.000009833634,0.00001968285,0.00009432033,0.00001233148,0.9911978,0.004203652,0.002464395,0.0006521964,0.000008855105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.205273,0.004551528,0.7778165,0.001058075,0.0002818697,0.0001752228,0.0006436206,0.004243887,0.00595624],"genre_scores_gemma":[0.951538,0.0009470254,0.04509921,0.0001664879,0.00008788879,0.00003377053,0.0004310856,0.00006185991,0.001634605],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008221831,"threshold_uncertainty_score":0.01634794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09846762901745469,"score_gpt":0.4608618084699151,"score_spread":0.3623941794524604,"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."}}