{"id":"W2901030517","doi":"10.1109/tmi.2018.2881415","title":"Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; University of Toronto","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Representation (politics); Pattern recognition (psychology); Generative adversarial network; Medical diagnosis; Computer vision; Machine learning; Pathology; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0006745582,0.0001847109,0.000206759,0.0001568417,0.0004679119,0.0000819102,0.0005610455,0.0001285036,0.000191178],"category_scores_gemma":[0.00006555361,0.000186533,0.000132699,0.0003153903,0.0004183268,0.000426743,0.000004925758,0.0004705806,0.00003865122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001164908,"about_ca_system_score_gemma":0.0001064994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000010732,"about_ca_topic_score_gemma":0.00002899032,"domain_scores_codex":[0.9980546,0.0001134796,0.0003267273,0.0005420692,0.0004242728,0.0005388386],"domain_scores_gemma":[0.9985769,0.0006481308,0.00008399526,0.0003316168,0.0001236507,0.0002356798],"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":[0.00003743071,0.00006820502,0.00001684407,0.0000135536,0.0000298639,0.00002789667,0.0008976382,0.01443097,0.0005532201,0.0004243978,0.0004656227,0.9830344],"study_design_scores_gemma":[0.0004707614,0.0000998187,0.00007960854,0.00004724074,0.00001873441,0.0001879317,0.0001163635,0.9957433,0.001326147,0.0003089927,0.001406498,0.000194589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008313025,0.00008658256,0.9901171,0.003560528,0.004449638,0.0001867471,0.000001934747,0.0005798272,0.0001863592],"genre_scores_gemma":[0.9615992,0.00001127122,0.03519203,0.002170274,0.0008325887,0.0001198776,7.249342e-7,0.00004517352,0.00002889799],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9828398,"threshold_uncertainty_score":0.7606593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02430001427786206,"score_gpt":0.2765578876928069,"score_spread":0.2522578734149449,"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."}}