{"id":"W3007471968","doi":"10.1109/globecom38437.2019.9014112","title":"From Whole to Parts: Medical Imaging Semantic Segmentation with Very Imbalanced Data","year":2019,"lang":"en","type":"article","venue":"","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Medical imaging; Natural language processing; Image segmentation; Computer vision","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":[0.003040187,0.001165307,0.0011475,0.003103414,0.0009923214,0.002073478,0.001443421,0.002164522,0.001439492],"category_scores_gemma":[0.005416484,0.0005473006,0.001010439,0.002664783,0.001380683,0.002653862,0.002363679,0.001896639,0.0007501345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188707,"about_ca_system_score_gemma":0.001109138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002568094,"about_ca_topic_score_gemma":0.003106503,"domain_scores_codex":[0.9985961,0.0002103592,0.0001062503,0.0005411286,0.0003663363,0.0001797618],"domain_scores_gemma":[0.9980831,0.0006416775,0.0003338376,0.0004843902,0.000298853,0.0001580891],"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":[0.002398956,0.0004989905,0.02477116,0.0004778847,0.000317431,0.001227104,0.0008727903,0.1489894,0.04837324,0.01012594,0.02299959,0.7389476],"study_design_scores_gemma":[0.00007383973,0.0002184219,0.01227762,0.000100197,0.0001416634,0.001522063,0.0003960302,0.877358,0.0384104,0.05435162,0.01507036,0.00007972543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2189449,0.003043516,0.7640286,0.003399497,0.0005730539,0.0002679156,0.001969972,0.003737819,0.004034691],"genre_scores_gemma":[0.7454605,0.0008673331,0.2436886,0.0011887,0.0005378352,0.0001472832,0.004818669,0.0005098064,0.002781231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003103414,"threshold_uncertainty_score":0.01607817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02199879129681337,"score_gpt":0.3305590102538319,"score_spread":0.3085602189570185,"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."}}