{"id":"W4286784827","doi":"10.48550/arxiv.1904.13281","title":"CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke\\n Lesion Segmentation","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Discriminator; Stroke (engine); Convolutional neural network; Ground truth; Pattern recognition (psychology); Magnetic resonance imaging; Diffusion MRI; Noise (video); Radiology; Medicine; Image (mathematics); Physics","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":[0.0008998149,0.0009598164,0.0005031353,0.0004341899,0.0002756704,0.0005559595,0.001003514,0.0009192672,0.002264945],"category_scores_gemma":[0.003287011,0.0004638605,0.0006730183,0.0003556386,0.0008822834,0.0006905099,0.001213443,0.001708043,0.0005336798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171564,"about_ca_system_score_gemma":0.00074561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008902267,"about_ca_topic_score_gemma":0.008166373,"domain_scores_codex":[0.9997113,0.0001017492,0.00001081345,0.00007810478,0.0000544309,0.0000434216],"domain_scores_gemma":[0.9991137,0.0005500462,0.00008315052,0.0001205314,0.00009137812,0.00004123309],"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":[0.00008095975,0.00001924478,0.0002972176,0.00001895009,0.00002431894,0.00004992302,0.00001991629,0.9765025,0.001854942,0.002797833,0.0008240984,0.01751],"study_design_scores_gemma":[0.00000169889,0.000005068945,0.00006129661,0.000002126425,0.00000236291,0.000006108538,0.00000129553,0.9975837,0.0006438639,0.00159018,0.0001002443,0.000001894782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08479968,0.0008260234,0.9040682,0.001016407,0.0001138057,0.00006919313,0.0004833604,0.004090753,0.004532619],"genre_scores_gemma":[0.8873737,0.0003937863,0.1034087,0.0003553148,0.00007644066,0.00009641778,0.001091234,0.0002991135,0.006905259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008902267,"threshold_uncertainty_score":0.01770085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.140853850240458,"score_gpt":0.2809411228166107,"score_spread":0.1400872725761526,"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."}}