{"id":"W3206505147","doi":"10.3389/fmed.2021.821120","title":"MEDUSA: Multi-Scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis","year":2022,"lang":"en","type":"article","venue":"Frontiers in Medicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"NOSM University; Thunder Bay Regional Health Sciences Centre; McMaster University; University of Waterloo","funders":"","keywords":"Computer science; Context (archaeology); Convolutional neural network; Architecture; Encoder; Artificial intelligence; Scale (ratio); Deep learning; Network architecture; Image (mathematics); Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006115824,0.0008860429,0.0004955532,0.000607321,0.000250626,0.0006871082,0.001475684,0.00111856,0.002852899],"category_scores_gemma":[0.001608125,0.0004220677,0.0006594745,0.0004134071,0.0003646245,0.001102154,0.001447966,0.001310872,0.001225911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007422761,"about_ca_system_score_gemma":0.0009919513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005770754,"about_ca_topic_score_gemma":0.01230652,"domain_scores_codex":[0.9997974,0.00004027705,0.000009084103,0.00006052706,0.00006213834,0.00003060542],"domain_scores_gemma":[0.9997442,0.00009419739,0.00002300685,0.00004004336,0.00006702209,0.00003153951],"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":[0.0004127664,0.0002991358,0.003623256,0.0002306432,0.0003261081,0.0003328834,0.0001421646,0.2936394,0.03027198,0.008955078,0.03593021,0.6258364],"study_design_scores_gemma":[0.00001870039,0.00006133341,0.0004635712,0.00001352983,0.00002298338,0.00008670513,0.000007897119,0.9883454,0.00426371,0.004049492,0.002654247,0.0000124425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04352982,0.002871726,0.9297994,0.001185786,0.0002713776,0.0001752953,0.0009396104,0.01601189,0.005215085],"genre_scores_gemma":[0.5739208,0.001297704,0.4034818,0.002121419,0.0002253037,0.0002952295,0.003351929,0.0006771393,0.01462879],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005770754,"threshold_uncertainty_score":0.01147431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01364267086996908,"score_gpt":0.3097856127581687,"score_spread":0.2961429418881996,"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."}}