{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000218571,0.000121082,0.0002329515,0.0000724598,0.00002703875,0.00003936248,0.0002191351,0.00003772372,0.001609937],"category_scores_gemma":[0.0002113936,0.00009153641,0.00001856059,0.0001734138,0.0000281149,0.0002042551,0.0001921556,0.0001211228,0.001015903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009669711,"about_ca_system_score_gemma":0.0002220768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001151813,"about_ca_topic_score_gemma":0.0001792285,"domain_scores_codex":[0.9984918,0.00002679566,0.0001859825,0.000481181,0.0006018372,0.0002124608],"domain_scores_gemma":[0.998527,0.0002450672,0.0000398273,0.0009156298,0.00004869411,0.0002237729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003749627,0.0003855899,0.6368745,0.0001768713,0.0001677153,0.0002660537,0.001042767,0.0001054857,0.02126496,0.0000213128,0.3195206,0.01979915],"study_design_scores_gemma":[0.02023476,0.0007778365,0.334355,0.005691597,0.0008420857,0.000136437,0.004419489,0.1338491,0.0247375,0.0001589825,0.4734573,0.001339904],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8269712,0.00008078245,0.009139551,0.1619697,0.0003649914,0.0006489635,0.00004060984,0.000215022,0.0005692164],"genre_scores_gemma":[0.9174582,0.00001125804,0.004463129,0.07639013,0.0003003613,0.00001661157,0.0007011715,0.00003161784,0.0006274875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3025195,"threshold_uncertainty_score":0.9997619,"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."}}