{"id":"W3168214987","doi":"10.1017/cjn.2021.91","title":"Synthesis of glioma histopathology images using generative adversarial networks","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Discriminator; Artificial intelligence; Deep learning; Generative grammar; Generator (circuit theory); Set (abstract data type); Pattern recognition (psychology); Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005834764,0.0005886301,0.0002847581,0.0004400988,0.0001342822,0.000657491,0.0004805574,0.0006304657,0.002299533],"category_scores_gemma":[0.001568217,0.0003249674,0.0006973084,0.0002440203,0.0004519306,0.0004640527,0.000702141,0.000947359,0.0004658166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005970257,"about_ca_system_score_gemma":0.0003581472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001118165,"about_ca_topic_score_gemma":0.00146944,"domain_scores_codex":[0.9998172,0.00003603954,0.00000820749,0.00004178244,0.00007589476,0.00002083741],"domain_scores_gemma":[0.9996083,0.0002130647,0.00004658964,0.00006340942,0.00004662369,0.00002200027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001236867,0.00003756766,0.0007901836,0.0001305675,0.00004900028,0.000289559,0.0000998988,0.8887389,0.03051647,0.01180994,0.003263741,0.0641505],"study_design_scores_gemma":[0.0000104456,0.0000454311,0.000333782,0.00001838794,0.00000921446,0.0001521172,0.00001545409,0.9775159,0.01220935,0.006734263,0.002941627,0.00001392905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04605261,0.0003645466,0.9447253,0.0006190449,0.0001566389,0.0001209641,0.0004663103,0.001381484,0.006112989],"genre_scores_gemma":[0.6882981,0.0005787707,0.3008895,0.0004086781,0.00006913894,0.0001660162,0.0009097138,0.0003962582,0.008283825],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002299533,"threshold_uncertainty_score":0.007692754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03291810227836283,"score_gpt":0.2611222735288195,"score_spread":0.2282041712504567,"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."}}