{"id":"W3207280867","doi":"10.18280/rces.080302","title":"A Combined Image Segmentation and Classification Approach for COVID-19 Infected Lungs","year":2021,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pneumonia; Image segmentation; Artificial intelligence; Segmentation; Computed tomography; Computer science; Pattern recognition (psychology); Image (mathematics); Lung; Computer vision; Medicine; Radiology; Disease; Pathology; Internal medicine; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007842507,0.0006902668,0.0008004511,0.002577368,0.0004219659,0.00119935,0.0008307612,0.001206383,0.001205861],"category_scores_gemma":[0.001092202,0.000387832,0.001085457,0.001090091,0.0004459556,0.0008731455,0.0005023523,0.0006034683,0.0005161478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000426603,"about_ca_system_score_gemma":0.0006001241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003051011,"about_ca_topic_score_gemma":0.002995474,"domain_scores_codex":[0.9996111,0.00005232491,0.00004314301,0.0001136395,0.0001229548,0.00005685918],"domain_scores_gemma":[0.9995882,0.000114374,0.00004453158,0.000031246,0.0001932579,0.00002830265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003804632,0.0001547764,0.005872699,0.0004453872,0.0001573427,0.0004628831,0.0003115622,0.06258463,0.09963442,0.002224221,0.001988269,0.8257834],"study_design_scores_gemma":[0.00001815231,0.0002497344,0.006372258,0.0000504302,0.0001461745,0.0007303415,0.000195395,0.957156,0.02841361,0.002676293,0.003949965,0.00004164383],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04359102,0.001808451,0.9511724,0.0003549877,0.0001144627,0.0001439252,0.00009873191,0.0008420961,0.001873937],"genre_scores_gemma":[0.3921067,0.002124227,0.6011214,0.0003023348,0.0001883903,0.0001453983,0.0003670995,0.0001144503,0.003530073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003051011,"threshold_uncertainty_score":0.006066501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05107939260949537,"score_gpt":0.3645067333006402,"score_spread":0.3134273406911448,"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."}}